An Ecology of Social Categories
Elizabeth G. Pontikes,a Michael T. Hannanb
a) University of Chicago; b) Stanford University
Abstract: This article proposes that meaningful social classification emerges from an ecological dynamic that operates
in two planes: feature space and label space. It takes a dynamic view of classification, allowing objects’ movements in
both spaces to change the meaning of social categories. The first part of the theory argues that agents assign labels
to objects based on perceptions of their similarities to existing members of a category. The second part of the theory
shows that an object’s perceived similarity to members of other categories reduces its typicality in a focal category. This
means that for categories with a high degree of overlap with other categories in label space (lenient categories), the
link between feature-based similarities and labeling weakens. The findings suggest that social classification will likely
evolve to contain both constraining and lenient categories. The theory implies that this process is self-reinforcing, so that
constraining categories become more constraining, whereas lenient categories become more lenient.
Keywords: categories; features; labels; leniency; patents; software
Editor(s): Olav Sorenson; Received: April 15, 2014; Accepted: May 28, 2014; Published: August 18, 2014
Citation: Pontkes, Elizabeth G. and Michael T. Hannan. 2014. “An Ecology of Social Categories.” Sociological Science 1: 311-343. DOI: 10.15195/v1.a20
Copyright: c 2014 Pontikes and Hannan. This open-access article has been published and distributed under a Creative Commons Attribution License,
which allows unrestricted use, distribution and reproduction, in any form, as long as the original author and source have been credited. c b
ategories structure social domains. They
C
locate actors in comparative sets and delineate standards for evaluation. Social agreement
about what constitutes membership in categories
imposes constraint that induces actors to conform to categorical types. This is an instance of
a general dynamic that has long interested sociologists: social structures constrain individual
actors, then widespread conformity leads to the
replication of the structures.
Research documents the homogenizing effects
of social categories. Organizations adopt similar
procedures, architectures, and even identities, not
only because the practices are effective but also
to conform to taken-for-granted ideas about how
to organize. What drives this dynamic is that objects have less appeal and receive fewer rewards
when they do not fit into an accepted category
(Zuckerman 1999; Hsu 2006; Hsu et al. 2009; Leung and Sharkey 2014). In this view, complying
with categorical imperatives provides benefits. As
a result, actors conform to categorical standards
and existing category codes get replicated. This
rendering depicts agents as navigating a static
category structure when deciding how to classify
or make sense of objects and actors.
sociological science | www.sociologicalscience.com
There is reason to question whether categorical structures tend toward stasis. The vibrant
lines of research on categories and markets build
on the view that audiences and market intermediaries create categories. Audience members come
to agree on labels and codes as they seek to interpret action in a domain. Previous research has
focused on how agreed-upon categorical meanings affect perceptions and evaluations. But the
converse also holds: the characteristics of objects that get categorized shape meanings. For
example, when telephone technology migrated
from rotary-dial to touch-tone, the meaning of
<telephone> evolved to include the new technology.1 More generally, when an object changes its
characteristics or gets reclassified, this potentially
changes the meaning of one or more categories.
This means that social categories are less durable
than we generally assume.
The question of whether social structures affect the behavior of individual actors, or whether
individual behaviors shape social structure, is
pertinent to many areas in sociological research.
Scholars studying institutions and organizational
forms maintain that social structures are homog-
311
1 Throughout we mark terms from the language of the
domain, the so-called object language, with angle brackets.
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
enizing and self-replicating while acknowledging
that these structures—and the meanings they
invoke—frequently change. This tension has been
extensively discussed as an important unresolved
issue in organizational sociology (Romanelli 1991;
Clemens and Cook 1999). That social structures are simultaneously rigid enough to induce
widespread conformity, and malleable enough to
change as a result of nonconformist behavior,
presents an underlying tension in the literature.
Recent work on classification sidesteps this
issue by assuming that category boundaries are
policed by market intermediaries such as stock
market analysts (Zuckerman 1999), credit agencies (Ruef and Patterson 2009), critics (Hsu 2006;
Rao et al. 2003; Negro et al. 2010; Negro and Leung 2013), and government agencies (Ruef 2000).
But what has historically interested sociologists
in categorization is that macro-level structures
emerge through uncoordinated individual beliefs
about social meaning embedded in objects or
concepts (Berger and Luckmann 1967; Durkheim
1931; Meyer and Rowan 1977). For instance, sociological studies of markets show that stable
market structure can arise when organizations
independently take market positions in response
to positions taken by competitive rivals (White
1981; Porac et al. 1995). These ideas also underpin recent work that models the emergence
of categories on the basis of audience members
coming to agreement about schemas that capture
the bases of similarity among objects (Hannan
et al. 2007).
We suggest that meaningful classification can
emerge even in an evolving environment comprising loosely coordinated actors. This occurs
when people observe current categorization, make
similarity judgments based on objects’ features,
classify them accordingly, and share their classifications. If the process is not coordinated, some
objects might get reclassified frequently. This can
change the meanings of categories, which then
alters how objects with stable categorizations
get interpreted. It might seem that this type of
process would lead to chaos, with meanings and
memberships changing too rapidly to coalesce
into a coherent system of categories. In contrast,
we show that classification can emerge even when
objects change features and categorical meanings
evolve.
sociological science | www.sociologicalscience.com
We are struck that this will produce an ecological dynamic for social categorization that operates across two planes: feature space and label
space. Feature space locates objects by their
feature values. Label space contains labels for
categories. The two planes are connected when
agents apply a label to a profile of feature values.
When an audience reaches consensus about the
link between a label and a set of features, the
label denotes a category.
Associations between category labels and features are straightforward in a static world. But
categorization becomes more complex when we
consider that the social meaning of categories
can change. For example, when many category
members alter their characteristics, this shifts
how people conceive of the category. This means
a focal category member who was once typical
of that category might become atypical. Such an
outcome characterizes obsolescence processes. A
scientist who still teaches and researches what
she learned decades ago might have been a highly
typical member of her discipline as a new graduate but very atypical today. More generally,
changes in feature values by some members can
affect categorization for others.
A similar dynamic plays out for labeling. If
atypical entities are assigned membership in a
category—as when producers of processed foods
start being labeled as <natural>–then the category will become less crisp. As a result, existing
members may no longer be considered typical of
the category. For instance, executives of organizations with nanotechnology capabilities concluded
that their organizations should not be included in
the <nanotechnology> category when the label
was used so broadly that its underlying meaning
no longer reflected their companies’ capabilities
(Granqvist et al. 2013).
We study these dynamics by building an explicit theoretical model of the coupling of feature
space and label space. Our model relies on two
processes, both based on similarity judgments.
The first involves the similarity of a newly observed object to the current membership of a
category. We argue (and show empirically) that
feature-based similarities strongly influence label
assignments even as actors change positions in
both spaces. As objects move, conceptions of a
category can change, and so can its membership.
312
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
But a link between features and labels persists.
In the <telephone> example, the sets of features
associated with this category have evolved from
rotary dial to touch-tone to touch-screens and
small hand-held electronic devices. The specific
characteristics associated with the concept have
changed, but it still retains a well-defined meaning. The second process underlying our model
concerns the similarity of a focal category to other
categories. Our theory and empirical findings
imply that extensive overlap in category memberships weakens the link between feature values
and label assignments. This can be seen in the
example of <nanotechnology>, which evolved to
overlap with a number of scientific disciplines
including organic chemistry, molecular biology,
semiconductor physics, and so on. Not only does
the meaning of this category evolve, so too does
the strength of the link between the label and set
of features expected of category members.
In addressing these issues we employ a formal language proposed by Hannan et al. (2007)
and further elaborated by Hsu et al. (2009) and
Kovács and Hannan (2011). We think a formal
theory is needed because these notions can be
slippery and erroneous reasoning can easily result. Formal languages work against tendencies in
natural language to skip steps and misidentify required assumptions, which can lead to imprecise
conclusions.
by restricting the term “label” to refer to tags
that are paired with meanings. We often refer to
labels as shorthand for the concepts/categories
that they designate.
We construct a theory for the general case
in which agents, as audience, classify producers.
Audience and producer are paired roles, and in
many contexts the same actors play both roles.2
That is, producers can be audience to each other.
We do not specify a particular kind of audience
because our assumptions plausibly describe processes that apply for many kinds of agents playing
an audience role, including critics, consumers, rivals, and the producers themselves.
We build our argument at the level of an individual person as an audience member. This focus
cuts a layer of complexity that arises because
different audiences have different perspectives on
classification (Pontikes 2012; Hsu and Elsbach
2013). For example, RottenTomatoes.com and
IMDb.com agree in classifying Mel Brooks’s sendup of the western Blazing Saddles as <comedy>
and <western>. But Rotten Tomatoes classifies
The Blues Brothers as <action/adventure> and
<comedy>, while IMDb classifies it as <comedy>, <music>, and <musical>. Our analysis
considers the issues from the perspective of individual members of an audience.
Feature Space and Label Space
Category Labels in a Domain
Labels play a central role in our analysis because
they denote concepts, mental representations of
similarity and difference (Murphy 2002). Although such representations need not be paired
with labels, they often are. In the case of social
classification, we generally expect concepts to
labeled, otherwise people could not easily communicate about what they represent. Sociological
analysis focuses on categories, where a label not
only represents a concept for a particular individual but also conveys broad agreement about
the pairing of a concept with a label, which gives
the label a social meaning. Because people use
labels to communicate about concepts and categories, much useful analysis can be done with
primary reference to their labels. For this reason
we can simplify the language of our argument
sociological science | www.sociologicalscience.com
313
The received view of classification treats labels as
tied to fixed sets of feature values. If the relationship between labels and features remains static,
then it is sufficient to model a single categorical
space that individuals and organizations navigate.
But once we allow the meanings of categories to
change—and the possibility of change to be contingent on how actors navigate the categorical
structure—it is necessary to model the potentially
variable connections between the spaces. We aim
to explore how actors’ movements affect the stability of social classification. So, the bedrock of
our theory is a model of two spaces, feature space
and label space, and the mapping that connects
them. Feature space and label space are instanti2 Scientific research provides a familiar example of
producers actively playing both roles, as researchers frequently alternate between author and reviewer roles.
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
ations of what Gärdenfors (2004) calls conceptual
spaces (see also Widdows 2004).
Research in psychology presents three standard accounts regarding the mental representation of concepts. A representation is stored
as (1) a prototype, a “best example,” (2) exemplars, known images of the concept, or (3) a
schema, a structured view of a concept (Smith
and Medin 1981; Murphy 2002). All three perspectives agree that an object’s observable characteristics determine categorization. Research on
social categories as part of a cultural system has
concentrated on the third case, where concepts
are stored as schemas, which tell the patterns of
feature values that closely fit the concept (DiMaggio 1997). We follow this view in constructing
models.
We represent producers by their positions in
a feature space of observable characteristics. Producers can lie at varying distances from each
other, and they move in this space when they
change their feature values. In formal terms, we
let F denote the space of the combinations of
feature values relevant to agents in a domain.3
For this (and many other sociological applications) feature space can be represented as a
weighted graph. Such a representation defines
nodes as feature profiles, combinations of feature values. Suppose that there are K relevant
features in a particular context. Then the set
of positions is the set of an ordered K-tuple of
feature values. For instance, if the features are binary, then there are 2K positions (vertices in the
graph space). We can specify this type of space
using a graph relation that tells which vertices
are accessible from which other vertices. To represent this, the crucial issue concerns how many
features can be changed at one time. Analysis is
greatly simplified if we assume that only one feature value can be changed at a time.4 This means
that the edges in the graph connect positions that
differ on only one feature value.
To fix ideas, consider Figure 1, which depicts
the graph for a space defined for three binary fea3 In our construction a feature is a dimension such as
the form of authority in an organization. Positions in
feature space are vectors of the values of a set of features,
for example, traditional, rational-legal, and charismatic
forms of authority.
4 Hannan et al. (2003) discuss these issues in the context
of organizational change and consider ways of relaxing
this constraint.
sociological science | www.sociologicalscience.com
tures (with the restriction that edges connect only
those vertices that differ by one feature value).
Each of the eight vertices gives a profile of feature
values. For example, (0,0,0) describes the position for which each of the three relevant features
has the value of zero. The edges connect vertices
that differ on only one value, for example, the
vertex (0,0,0) is connected to (1,0,0), (0,1,0), and
(0,0,1). The vertices in this diagram are what we
call feature profiles.
In a standard graph space, distance is given by
path length, specifically the length of the shortest
path that connects a pair of nodes. In our construction, each edge is associated with a transformation distance, a real-valued weight that refers
to the scale of the change required to move from
one configuration to another (Hahn et al. 2003).
We define a distance between positions (vertices)
as the scale of transformation required to convert
from one set of feature values to another. So
the distance from (0,0,0) to (1,0,0) is the scale of
the transformation needed to change the first feature from 0 to 1. If this feature is, say, the form
of authority, then a much deeper organizational
transformation is required to shift from traditional to rational-legal (to use two of Weber’s
types) than to change more technical features.
We treat distances as subjective judgments
from the view of a focal agent and also allow the
possibility that the structure of the space morphs
over time. Precision demands that we tune the
formal notation to fit these considerations by
including argument slots for the agent and the
time point in the distance function. Economy
of expression argues the opposite. We use the
more economical representation and omit from
our notation the dependence of each predicate
and function on these arguments. But each function and predicate should be considered as dependent on the agent and on the time point. With
this convention we express distance in feature
space as dF (v, v 0 ), the scale of the transformation
required to convert from configuration v to the
configuration v 0 . The pair hF, dF i defines the
metric feature space.
The second space of interest is also a graph
space, one whose vertices are labels. We refer
to this space as a label space, represented as L.
Distances in this space, denoted as dL (l, l0 ), tell
the distances between labels. Again we allow
the possibility that distances between adjacent
314
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
(1,1,0)
(1,1,1)
(1,0,1)
(1,0,0)
(0,1,0)
(0,1,1)
(0,0,0)
(0,0,1)
Figure 1: Example of a feature space with three binary features.
vertices might differ. The pair hL, dL i defines the
metric label space.
We need to control the scope of label space,
because social actors use many kinds of labels.
Some of them are related, but others are not, for
example, a person might be labeled as <Hungarian>, <logician>, and <archer>. This difference
matters, because movements among related categories can affect the structure in a way that moves
among unrelated categories do not. For theoretical analysis, it suffices to restrict the scope of the
arguments to cases in which the language has a
relatively simple structure. We use the notion
of a domain, a super-category whose included
categories compose the set to be analyzed (Hannan et al. 2007). Within such a structure, the
schemas for all of the subcategories contain the
codes for the supercategory’s schema (along with
other differentiating codes). These categories are
related within the language of the audience that
makes these distinctions. The scope of our theory
is restricted to a single domain.
In addition, we restrict the scope of label
space to include labels at the same hierarchical level. If we do not do this, then analysis of
the interdependence of movements among categories gets very complicated without shedding
sociological science | www.sociologicalscience.com
315
additional insight. We limit the scope to sets of
categories within a domain that satisfy the restriction that none of the categories considered in
the analysis is a subcategory of any other. When
we refer to a label, it should be considered to be
the tag for a category that is an element in the
common language of the focal domain.
Relation between the Spaces
Social classification emerges from the association
of labels with features through schematization. A
key element of our theoretical development—one
not present in other work on social categorization—
is that we define explicitly the mapping between
features and labels. Our conceptualization builds
on the following considerations about the nature
of schematization:
1. Schematization ties labels to sets of featurevalue profiles.
2. The mapping is partial; not all feature profiles are schematized.
3. The agent’s language is coherent in the
sense that different labels point to distinct
collections of profiles. That is, the sets of
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
profiles (not particular feature values) associated with different labels do not intersect.
4. Schematization preserves distance, meaning
that distance in one space can be inferred
from distance in the other.
σ : L 7−→ Φ,
and
We can make these abstract ideas somewhat
more concrete with an example. Figure 2 depicts
two graphs and a mapping connecting them. The
feature space contains five feature profiles denoted
a through f (each individual profile is a set of
feature values, as illustrated in Figure 1). Again
the edges connect profiles that differ on only one
feature value. The spacing between vertices indicates the transformation distance between them.
The label space contains three tags: 1, 2, and 3.
The morphism we want to characterize maps a
and b to 1 and c to 2, d and e to 3, and does not
associate a label with f . The absence of edges
connecting labels preserves the absence of edges
between schemas. The set of schemas that have
a common label are shaded in the figure. The
dotted lines display the details of the mapping.
The next step is to provide a formal characterization of the relation between the spaces.
Intuition suggests that the mapping be defined
from feature space to label space because the existence of clusters of feature values induces labeling
(Hannan et al. 2007). However, consideration of
the list of desiderata (and the figure that exemplifies them) suggests otherwise. All labels are
schematized, but not all combinations of feature
values belong to a schema. We can obtain the
first three desiderata by defining a one-to-one
mapping from labels to sets of feature-value profiles. Such a mapping yields an isomorphism.
If we add that the mapping preserves structure,
namely, distance, then the isomorphism has the
form of an embedding.
Definition 1 (Isomorphic embedding of sets of
profiles in feature space in the space of labels).
Let Φ ⊆ P(F ) denote a subset of the powerset of
the feature-value profiles,5 and L denote the set
of labels used by the focal agent for the entities
in the domain. We call σ a distance-preserving
isomorphic embedding of the power set of feature profiles in the set of labels if the following
5 The powerset of a set is the set, P(·) is defined as the
set of all of its subsets. In the case of the set x = {a, b},
P(x) = {∅, {a}, {b}, {a, b}}.
sociological science | www.sociologicalscience.com
conditions hold:
316
∀φ1 , φ2 , φ3 , φ4 , l1 , l2 , l3 , l4 [(σ(l1 ) = φ1 )
∧(σ(l2 ) = φ2 ) ∧ (σ(l3 ) = φ3 ) ∧ (σ(l4 ) = φ4 )
→ (dF (φ1 , φ2 ) = dF (φ3 , φ4 ))
↔ (dL (l1 , l2 ) = dL (l3 , l4 ))].
The second clause ensures that the mapping preserves distance. It states, using formal language,
that if the feature space distance between φ1 and
φ2 is the same as the distance between φ3 and
φ4 , the distance between their respective labels
is also the same. This embedded isomorphism
provides the following novel representation of a
schema.
Definition 2 (Schema). An agent’s schema for
a category is the subset of the feature space that
the agent associates with its label (under the
isomorphic embedding): Sch(λ, l, y) ←→ λ =
σ(l, y).
We must add an auxiliary assumption that
the agents engage in schematization along the
lines of the preceeding construction.
Auxiliary assumption 1. The agents in the
audience apply labels to schemas in a way that
corresponds to the isomorphic embedding, σ.
We restrict our analysis to situations in which
the focal agent’s schematization can be represented by a distance-preserving isomorphic embedding as in Definition 1. Instead of invoking
the predicate Sch(λ, l, y) whenever we introduce
a label in a formula, we use the notational shorthand that the symbol λy refers to a schematized
label for the agent y.
A schema might be a singleton, but generally
schemas have higher dimensionality. We define
the distance between a point in feature space
and a schema using the standard definition of
the distance between a point and a set: dF (f, λ)
is defined as the minimum of the distances of
the point to each member of the set. When it
comes to the distances between schemas, we need
a distance metric appropriate for sets: Hausdorff
distance. In the case of the schemas λ and λ0 ,
this metric, denoted as H(λ, λ0 ), is constructed
as follows. First choose the element in λ that is
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
L
F
a
1
c
b
2
f
e
3
d
{a,b}→{1}
{c}→{2}
{d,e}→{3}
Figure 2: Example of an isomorphic embedding of schemas (sets of feature values) and labels.
closest to the set λ0 and record this (minimal)
distance; then calculate the minimal point-to-set
distance from λ0 to λ. The Hausdorff distance
between the sets is the maximum of the two
minimal point-to-set distances.6
It follows from our construction that the distance between a pair of labels equals the Hausdorff distance between the schemas associated
with them under the isomorphic embedding. This
construction yields an implicit definition of distance in label space as the Hausdorff distances
between the associated schemas.
This defines an agent’s schema, which links
feature profiles with labels. Whether particular
sets of profiles become associated with a label is
another question. Of course, a theoretical model
cannot predict this, because the audience determines the sets of features that become a label’s
schema. Recent sociological work on classification
6 More
formally,
H(λ, λ0 ) ≡
(
max
)
supv∈λ inf
v 0 ∈λ0
dF (v, v 0 ), sup
v 0 ∈λ0
inf
dF (v, v 0 )
,
v∈λ
where inf and sup refer to the infimum and supremum,
respectively.
sociological science | www.sociologicalscience.com
317
has relied on experts, for example critics or government agencies, to assign objects to a category—
or to provide the category’s extension.7
Our argument does not require external agents
to maintain categorical boundaries. Rather, we
suggest that the link between the spaces builds
on the characteristics of the objects that are already labeled (those in the category’s extension).
Consider the example in Figure 2. Suppose that
there are three producers and that x1 has the
feature profile a, x2 has b, and x3 has c. Now
suppose that a focal agent applies the tag 1 to
x1 , x2 , and x3 (so the agent’s extension of this
label consists of these three producers). What
has happened here is that the focal agent has
7 Logicians and linguists define concepts in two ways:
extensional and intensional. The extension of a concept
refers to the set of objects that satisfy the concept in
one fixed context. The intension of a concept refers to
its meaning over possibly changing contexts (alternative
possible worlds). The extension of a concept is an actual
set of members; in the intentional view a concept has a
more abstract characterization. For instance, consider
how we would define the concept <prime number>. We
can give its (partial) extension as {3, 5, 7, 11, . . .}. The
standard meaning (or intension) of this concept is “a
natural number greater than one that is divisible only by
one or itself.”
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
reacted to the partial membership of x3 in the
agent’s schema for the label and has assigned this
producer membership. Other agents, on learning
of the first agent’s extension, might experience
positive updates in the probability of associating
the feature profile c with the tag 1. So the link between features and labels emerges endogenously
from the positions of objects in each space. As
objects change features and as label assignments
change, the link between feature space and label
space can also change.
cognitive science over the past 35 years has found
that people do not classify social and material
objects in such a crisp fashion. Rather, they
see some objects as having partial memberships,
which means that concepts can have a typicality structure. For example, apples and oranges
are judged as typical <fruit> but tomatoes and
olives are atypical (Rosch and Lloyd 1978). In
the case of markets, an independent business that
serves espresso and sandwiches might be considered “sort of” or “technically” a <restaurant> but
primarily a <coffee shop>. A software producer
that develops spreadsheets might have partial
membership in <database>.
The notion of a graded membership or typicality refers to the degree to which the focal agent
regards a producer’s feature values as fitting her
schema for the label (or the similarity to the prototype). Producers with feature values close to
the schema/prototype are said to be typical of
that agent’s concept.
In models of categorization, whether a person
applies a label to an object is based on the distance between her mental representation of the
object and her representation of the respective
concept (e.g., schema or prototype). The higher
an object’s typicality, the higher the probability
that the agent regards it as an instance of the
associated concept (Rosch and Mervis 1975; Tversky and Gati 1978). For example, restaurants
with similar menus get classified alike (Kovács
and Johnson 2014). This means that whether an
object gets assigned a label declines monotonically with its distance from the schema/prototype.
We propose a simple functional form for this relationship.
Categorization: Assigning
Labels to Producers
Next we turn to the issue of categorization: the
assignments of labels to objects (producers in our
case). We start by considering the link between
feature space and label space. We then build our
model for the standard view of category statics.
We subsequently extend it to consider category
dynamics.
Consider the producer’s location in feature
space, which we denote by fx . Refer again to
the example in Figure 2. An agent will normally
assign the tag 1 to a producer with feature-value
profile fx = a. But a, as drawn in this figure,
lies one step from b (in the same equivalence
class) and one step from c (not in the class) and
the dF (a, c) is not much greater than dF (a, b).
Producers with the feature profile a plausibly
could be assigned partial memberships in the
categories tagged as 1 and 2. Likewise, the profile
f (which is not associated with a label in this
example) lies one step from c and one step from
e—it stands between the two categories. Perhaps
it will be assigned partial memberships in 2 and
3. So, a producer’s distance from the schema
associated with a concept influences whether it
gets assigned full or partial membership.
Considering partial memberships marks a departure from classical understandings of concepts
and categories. In the so-called classical perspective, categories are crisp: objects either bear a
label or they do not, and they either fit a schema
or do not.8 For example, every natural number
is either a prime or not, and no prime number
is more prime than any other. But research in
8 More precisely, in the classical view, both the extension and intention of a category are crisp.
sociological science | www.sociologicalscience.com
Postulate 1. An object’s typicality with respect
to a concept is a negative logistic function of the
distance of its feature profile from the agent’s
schema for the concept label:9
N l, y [(λy 6= ∅) → ∃! a, b ∀x [(a > 0) ∧ (b > 0)
F
∧(T y(l, x, y) = (1 + a eb d (fx ,λy ) )−1 )].
9 The model we develop is inspired by Hampton (2007)
and Verheyen et al. (2010) but is adapted to accommodate
an argument about leniency. In particular, Verheyen et al.
(2010) build a Rasch model of categorization to implement
Hampton’s notion of agent-level thresholds, which we
do not include. The negative logistic is more suitable
for our purposes because it has a classic probabilistic
interpretation.
318
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
(In informal language, this postulate states that,
normally, a producer’s typicality to a schematized label l is a negative logistic function of the
distance between the producer’s feature profile
and the audience member’s schema for l. The
formula specifies the existence of the positive constants included in the logistic function and that
the formula holds for all labels and all audience
members in the domain.)
Here and elsewhere, N denotes a nonmonotonic quantifier. Formulas quantified in this way
provide formal representations of generic rules
(or rules with possible exceptions). Such rules tell
what is normally the case. The semantics of this
quantifier are spelled out in Pólos and Hannan
(2004). In addition, we use an informal sorting of
variables where l refers to a label, x to an object,
and y to an audience member.
Statics
Postulate 1 has implications for labeling. First we
develop the standard view, which we will call the
static perspective. In this view it is assumed that
both the category schema and the characteristics
of objects do not change. The main question is
how atypical objects are assigned membership in
categories that have fuzzy boundaries.
Fuzziness in category boundaries arises because agents tend to form different extensions
over occasions. Sometimes they will include an
object in a category’s extension and sometimes
not (for objects with moderate typicality). Recognition of this tendency has led to the use of a
probabilistic notion of categorization. Theory
and research emphasize the probability that an
object sits in the extension of a concept (over
occasions). Let π(l, x, y) denote the probability
that the agent y currently will apply the label l
to the object x. We propose that this probability
is proportional to x’s typicality for the concept.
Postulate 2. The probability that an agent categorizes an object as a member of a category is
proportional to its typicality with respect to the
concept:
N l, y [(λy 6= ∅) → ∃! k ∀x [(k > 0) ∧ (π(l, x, y)
= k · T y(l, x, y))].
sociological science | www.sociologicalscience.com
319
This small argument implies that an object’s
feature-based distance from the relevant schema
affects how it gets labeled.10
Lemma 1.
P l, y [(λy 6= ∅) → ∃! a, b, k ∀x [(a > 0)
∧(b > 0) ∧ (k > 0) ∧ (π(l, x, y) =
F
k · (1 + a eb d (fx ,λy ) )−1 )].
Dynamics
Now we turn to dynamics. Here we allow that
both category schemas and object characteristics
can change. To model this, we need to shift from
considering static probabilities of categorization
to the hazards of adding and dropping labels. We
turn to the question of how an object’s typicality,
based on its feature profile, and the changing
properties of the categories affect the dynamics
of labeling, the hazards of adding and dropping
labels. We start by stating a straightforward
extension of the typicality argument.
Postulate 3. An agent’s hazard of newly applying a label to a producer normally increases with
the typicality of the producer’s profile of feature
values from the schema for the label; and her
hazard of dropping the label decreases with this
typicality:
N l, l0 , y ∀ x, x0 [(T y(l, x, y) > T y(l0 , x0 , y)) →
((l ∈
/ `x,y ) ∧ (l0 ∈
/ `x0 ,y ) → γ(l, x, y) >
0
γ(l , x0 , y)) ∧ ((l ∈ `x,y ) ∧ (l0 ∈ `x0 ,y ) →
δ(l, x, y) < δ(l0 , x0 , y))].
Then it follows that the hazards are determined by the distance between a feature-value
profile and the applicable schema.
Proposition 1. The hazard of an agent’s newly
applying a label to a producer presumably falls
with the distance of the producer’s profile of feature values from the agent’s schema; and the hazard of the agent’s dropping the label increases with
10 The implications of a set of rules with exceptions
are the logical consequences of a stage of a theory. Such
provisional theorems have a haphazard existence: what
can be derived at one stage might not be derivable in
a later stage. So the status of a provisional theorem
differs from that of a causal story. The syntax of the
language codes this difference. It introduces a “presumably
quantifier, denoted by P. Sentences (formulas) quantified
by P are provisional theorems at a stage of a theory (if
they follow from the premises at that stage).
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
this distance:11
porous and have vague boundaries. This might
modify the mapping between the two spaces.
P l, l0 , y ∀ x, x0 [(dF (fx , λl ) <
→
Categories with vague, porous boundaries pro((l ∈
/ `x,y ) ∧ (l0 ∈
/ `x0 ,y ) →
liferate
in the social world. Scholars who focus
γ(l, x, y) > γ(l0 , x0 , y)) ∧ ((l ∈ `x,y ) ∧ (l0 ∈ `x0 ,y ) on the cognitive processes underlying categoriza→ δ(l, x, y) < δ(l0 , x0 , y))].
tion emphasize that categories organize knowlProof. The rule chain that connects the antecedent edge and enable humans to structure their reality
and consequent is a simple cut rule, which carries (Murphy 2002). But sociologists who study “onover from first-order logic to the nonmonotonic the-ground” classification have noted that loose
logic we use. The cut rule holds that φ → ψ and overlapping categories appear in all types of
and ψ → χ logically implies φ → χ. Postulates 1 classification systems, even those formally conand 2 imply that typicality declines with distance structed by authorities, what DiMaggio (1997)
from the applicable schema. Our extension of called administrative classification. For example,
the static typicality argument to dynamics (pos- the International Classification of Diseases (ICD)
tulate 3) states that higher typically increases retained a number of ill-defined categories for
the hazard of adopting a label and decreases the causes of death, such as <eclampsia>, <convulhazard of dropping a label. This completes the sion>, or <hemorrhage>, to guard against either
chain that connects the antecedent and the conse- having too many unclassified cases or inflating
quent. The argument does not support a negative figures of “badly defined diseases” (Bowker and
rule chain, a chain whose links run from the an- Star 2000). From a perspective that stresses the
tecedent to the negation of the consequent. The benefits of concepts in allowing economic and
absence of such a rule chain means that the im- rapid cognition, such vague concepts might seem
to be pointless and counterproductive. However,
plication stated in the proposition is proven.
loose concepts exist precisely because classificaFor this proposition, we have not assumed tion addresses a messy reality.12 Loose concepts
that the schemas for labels remain fixed. Rather, and categories allow classification to incorporate
an agent’s schema is influenced by character- entities that otherwise would be excluded, withistics of the objects that currently belong to out jeopardizing the crispness of other concepts
the category. This means that schemas change or categories.
We want to model how vague categories can
when members move within each space, and these
affect
the ecological dynamic introduced above.
changes influence subsequent categorization. Thus
To
do
this, we need a formal definition of cateemerges a coupled ecology of market classificagories
with
vague, porous boundaries. We refer to
tion. Objects can independently change positions
categories
that
are close to many other categories
in feature space by changing their characteristics,
13
as
lenient.
or in label space by being reclassified, and this
Lenient categories might not provide a bachanges the link between feature space and lasis
for
boundary enforcement. A key intuition
bel space. Altered category schemas then cause
is
that
lenient categories therefore provide lessagents to change other categorizations, possibly
sharp
meanings.
We argue that this tendency
leading to cascades of changes.
dF (fx0 , λ0l0 ))
Leniency and Categorization
We might also expect that label-space properties
affect labeling. In contexts that lack mechanisms
for boundary enforcement, labels can become
11 Note that l and l0 are variables—not names—and we
have not stipulated that l 6= l0 . So the proposition applies
when there is a single label in the domain. This becomes
important when we modify the relationship in meaning
postulate 1 below.
sociological science | www.sociologicalscience.com
12 An extensive literature in logic, linguistics, and philosophy of science explores the role of vagueness in scientific
and natural languages. See Black (1937), van Demeter
(2010), and the papers collected in Keefe and Smith (1997).
13 The language of leniency suggests that observed patterns of overlap reflect constraints, that the differences
among categories in sharpness of boundaries and breadth
of overlap are not accidental. This judgment, of course,
involves an inference. In developing the core argument,
we focus on the descriptive information contained in patterns of overlaps. Once the presentation of the argument
is complete, we show that it implies that the patterns
of overlap generally do reflect constraint (as opposed to
accidental patterns of overlap).
320
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
arises from confusion caused by the similarity
of lenient categories to others. For example, entrants into <disk array producer> were already
identified with many other categories, including
<storage subsystems>, <RAID>, and <networkattached storage>. As a result, the label took
on diffuse and variegated meanings (McKendrick
et al. 2003). In the emergence of the nanotechnology field, the label became quite loose due to overlaps with several scientific disciplines, including
molecular physics, materials science, computer
science, and electrical engineering (Grodal 2011).
The possibility that a welter of categories applies
to some members makes it hard for agents to
interpret these memberships. We develop this
notion in terms of proximities in label space.
Our argument focuses on the distance of a
focal category from the other categories in the
domain, which we call total distance.
Definition 3 (Total distance of a category from
all others in the domain).
P
L
0
y) = l0 ∈L
P D(l,
Pd (l, l )
0
F
= l0 ∈L d (λy , λy ) = l0 ∈L H(λy , λ0y ).
Lenient categories have low values of D. We
do not suggest that any difference in leniency will
be consequential. Rather, we propose that there
is a threshold over which multiple overlaps make
a category difficult for agents to interpret. The
following meaning postulate formalizes this idea
and provides an indirect definition of leniency.
Definition 4 (Overall proximity to other-labeled
objects at the producer level).
P
P
P (l, x, y) = [ l0 6=l l0 ∈`(x0 ,y) dF (fx , fx0 )]−1 .
We then need to modify the standard assumption about how agents categorize (see Postulate 1). Specifically, we propose that high overall
proximity weakens an object’s fit to categorical
schemas.15
Meaning postulate 1. If one category is much
more lenient than another, then it normally is
less distant from other categories in the domain:14
N l, l0 , y ∃ w [(w > 0) ∧ (len(l, y)−
len(l0 , y) > w) → D(l, y) < D(l0 , y)].
The relationship just postulated can hold for
many values of w. We identify (and name as
w∗ ) the minimum value for which the relationship holds:
∗
0
w = inf{w | (w > 0) ∧ N l, l , y [(len(l, y)−
len(l0 , y) > w) → D(l, y) < D(l0 , y)}.
14 In
the nonmonotonic logic we use, postulates can
be strict (that is, first-order rules without exceptions)
or generic statements that hold in normal situations,
rules with possible exceptions—see the supplementary
appendix.
sociological science | www.sociologicalscience.com
We suggest that leniency affects the link between feature-space positions and labeling. This
is because categories do not exist in isolation.
The vast research literature on concepts builds
on the notion that categories play the cognitive
role of grouping similar items and differentiating different items. Categorization concerns not
only the match between an object and an agent’s
schema for one category but also the difference of
the object from the agent’s schemas for other categories. A metaphor can be seen in the concept of
cue validity, which characterizes the diagnosticity
of a feature value for membership in a particular
category. Cue validity is based on both how much
a feature is associated with a focal category and
how little it is associated with others (Rosch and
Lloyd 1978). Analogously, an object’s fit to a
schema is more diagnostic of membership if the
schemas for other categories are not proximate.
Central to our argument is that having high
proximity to many categories distorts perceptions
of categorization based on feature values. To develop this formally, we first define, for producercategory pairs, the feature-space proximity between a focal producer and other producers assigned labels of all categories other than a focal
category.
321
Postulate 4. When the domain contains multiple labels, the typicality of a producer is a decreasing function of its proximity to the members of
other categories in the domain and a decreasing
function of its distance from the schema for the
focal category:
N l, y [(NL,y > 1) ∧ (λl 6= ∅) →
∃! α, β ∀x [(α > 0) ∧ (β > 0) ∧ (T y(l, x, y)
F
= (1 + eαP (l,x,y) eβ d (fx ,λl ) )−1 )],
15 In introducing this consideration, we introduce a specificity consideration: that the postulate that follows holds
only when the domain contains more than one label. In
the following we use specificity considerations to manage
the inconsistency between the two indirect definitions of
typicality.
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
where NL,y denotes the number of domain labels
that the agent has schematized.
The typicality function in postulate 4 approaches the function given in postulate 1 as
P ↓ 0. As P increases, typicality falls toward
zero. This means that the higher an object’s
proximity to other categories, the weaker is the
effect of its distance from the schema on typicality. Figure 3 illustrates the effect of increasing
proximity in terms of this function. The upper
(solid) curve plots µ = (1 + 0.02e2d )−1 , and the
lower (dashed) changes the value of the multiplier of the exponential term from 0.02 to 0.05,
reflecting a much higher value of overall proximity. For the solid curve, where the producer has
low proximity to members of other categories,
an object at close distance to the schema results
in a high expected typicality. For the dashed
curve, where the producer has high proximity to
members of other categories, an object at the
same distance from the schema will have a lower
expected typicality.
While the co-presence of postulates 1 and 4
in the theory would create an inconsistency in
first-order logic, this need not be so for nonmonotonic logic. We resolve this potential conflict by
relying on the most characteristic feature of nonmonotonic logic that more specific rules override
less specific ones. By restricting the postulate to
cases in which a domain contains more than one
label, we have made postulate 4 more specific
than postulate 1. This more specific relationship
overrules the less specific one in the analysis of
multilabel domains.
Next we tie the argument about labeling to
leniency. The first step is to define producer’s
closest category. It simplifies the analysis greatly,
without distorting the intuition, to assume that
the closest category is unique for each producer.
Definition 5 (Closest category).
cl(l, x, y) ↔ ∀l0 [(l0 ∈ L) →
d(fx , λ0y ) > d(fx , λy )].
Next we need to tie producer-level arguments
to the category-level ones. We do so by considering pairs of producers that are alike in one important respect: the distance of their feature-value
profile to the schema of their closest categories is
the same. In other words, we consider pairs who
sociological science | www.sociologicalscience.com
322
fit the most applicable (best-fitting) schema to
the same degree. But the pairs also differ in an
important respect: their closest categories differ
in total distance to other categories.
Postulate 5. Producers that are proximate to
the schemas of categories that lie closer to other
categories in the domain normally have higher
total proximity to the members of all other categories in the domain (holding constant the degree
of fit to the schema for the closest category):
N l, l0 , y ∀x, x0 [(D(l, y) < D(l0 , y) ∧ cl(l, x, y)
∧cl(l0 , x0 , y) ∧ (d(fx , λy ) = d(fx0 , λ0y ))
→ P (l, x, y) > P (l0 , x0 , y)].
Together, postulates 4 and 5 imply that producers
that are close in feature space to a lenient label
are seen as less typical of that label, as compared
to producers who are equally proximate to a
constraining one.
Lemma 2. If two producers are equally close
to the schemas for their closest categories and
the closest category for one is sufficiently more
lenient than the closest category for the other,
then the producer with the more lenient closest
category presumably has lower typicality in its
closest category than does the other producer:
P l, l0 , y ∀x, x0 [(len(l, y) − len(l0 , y) > w∗ )
∧cl(l, x, y) ∧ cl(l0 , x0 , y) ∧ (d(fx , λy ) =
d(fx0 , λ0y )) → T y(l, x, y) < T y(l0 , x0 , y)].
Proof. The antecedent requires that the domain
contains more than one label because it refers
to a pair of labels that are applied to a pair of
producers such that each bears one label but not
the other. This means that specificity considerations dictate that the proximity-weighted typicality function (from postulate 4) applies. Given
that the antecedent specifies that the producers’ feature profiles stand at equal distance from
the applicable schemas, any differences in typicalities depends only on the difference in total
proximities for the producers. The antecedent
also tells that the difference in the leniencies of
the applied categories exceeds w∗ , which means
that postulate 5 implies that the producer close
to the lenient label has higher total proximity
to the members of other categories. Inserting
this inequality in proximities into the proximityweighted typicality function completes the chain
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
1.0
0.8
0.6
0.4
0.2
1
2
3
4
5
dHf,lL
Figure 3: Two illustrative membership functions showing the effect of a difference in proximity on the
relationship between distance from a category schema/prototype and the probability of labeling; see
text.
linking the antecedent and consequent. In the
absence of any opposing rule change given the
premise set, this completes the proof.
It follows that the link between a producer’s
proximity to a label and categorization is stronger
for constraining labels and weaker for lenient
labels.
Proposition 2. Agents have higher hazards of
adding and lower hazards of (newly) dropping the
label of a producer’s closest category when it is
constraining as compared to when it is sufficiently
more lenient:
P l, l0 , y ∀ x, x0 [(len(l, y) − len(l0 , y) > w∗ )
∧cl(l, x, y) ∧ cl(l0 , x0 , y) ∧ (dF (fx , l)
= dF (fx0 , l0 )) ∧ ((l ∈
/ `(x)) ∧ (l0 ∈
/ `(x0 , y))
0
0
→ γ(l, x, y) < γ(l , x , y)) ∧ ((l ∈ `(x, y))
∧(l0 ∈ `(x0 , y)) → δ(l, x, y) > δ(l0 , x0 , y))].
Proof. The rule chain supporting lemma 2 entails a difference in typicalities for the pair of
producers. A chain rule with the extension of
the standard typicality argument to the dynamic
situation, postulate 3, provides a positive rule
chain linking the antecedent and consequent. The
theory as stated does not give rise to any negative
rule chains that imply a different conclusion (a different ordering of the hazards of dropping labels).
Thus the nonmonotonic proof is complete.
A Context: Market Classification in the Software Industry
We investigate this model in the empirical setting of the software industry. In the software
16 We add the qualification to fixed sets of categories
because new categories might deviate from the pattern.
The argument that supports proposition 2
holds that a category’s structural position in
sociological science | www.sociologicalscience.com
label space affects how strongly it is linked to
feature space. Specifically, the relationship between feature-space positions and labeling weakens when labels are lenient. This provides a
theoretical basis for predicting loose coupling of
feature values and label assignments.
Perhaps the most interesting implication of
this argument concerns the trajectories of leniency and constraint in classification. Our argument suggests that lenient categories become
more lenient: agents are less likely to take into
account feature-based similarities when applying
lenient labels, which can lead to a diverse set
of agents being assigned a lenient label. This
will increase the proximity of its members to the
memberships of other categories, which will fuel
the cycle. This pattern does not hold for members of much more constraining categories. The
“Discussion” section sketches the argument (for
a fixed set of categories) that lenient categories
remain lenient and highly constraining categories
remain constraining.16
323
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
industry, an informal classification of product
markets has emerged through interaction among
producers, analysts, the media, and consumers.
It depends heavily on producer affiliation with
market categories. Consumers, employees, and
investors use category labels to find producers,
products, and services, as well as to stay abreast
of innovations. Organizations identify with labels primarily to communicate what they offer to
various audiences.
In making the shift to a specific context, we
also shift from a single agent to a collective, an
audience. This is because we relate labeling actions by a focal firm to similar decisions by others.
These analyses depend on an assumption that
the language is common within the audience, otherwise it would be feckless to search for the kinds
of effects our theory yields. The degree to which
this assumption characterizes audiences of interest is an interesting and important topic in its
own right (Pontikes 2012; Koçak et al. 2014). But
we do not address it here. What is important
is that the audience investigated, software producers, uses a common system of classification to
make sense of the domain.
We represent label space based on affiliations
with market categories made by software producers in press releases. Press releases are an
important medium for an organization to convey
what it does. Software organizations use press
releases to distribute news and create a public
face for media analysts, investors, and consumers.
The major stock exchanges in the United States
suggest or require that companies issue press releases to distribute information. Media often note
press releases in their coverage: a study of public
companies finds an average of 1.5 (median 1.3)
media articles per press release issued from 2001
to 2006 (Soltes 2009). Product classification is
based on attributes of a company’s product or
service, so labels in this context are verifiable by
consumers or analysts. As a result, it is important
for organizations to describe their activities accurately. Because press releases can be produced
at low cost, even small and young organizations
(which are difficult to track otherwise) issue them.
For these reasons, press releases provide a good
source of data to construct label space in this
domain.
Software producers use category labels to describe themselves in press releases. For exam-
sociological science | www.sociologicalscience.com
ple, MicroStrategy identified itself in 1995 as “a
leading supplier of client/server decision support
tools,” thereby affiliating with two category labels. The <client/server> label indicates that
MicroStrategy’s software would run on multiple
client machines that communicated through a
server. The <decision support> label conveys
that the software provides reports that combine
data from a number of sources to identify and
solve problems and support business decisions
(Figure 4 lists other examples of label affiliations
from press releases in these data). In this context,
producers typically develop a suite of software
products targeted at one or more market categories, and the category affiliation is at the level
of the organization.
We investigated whether labels claimed in
press releases were also used by other important audiences. We found that the preeminent
industry-analyst organization, Gartner, issued reports on over 50 percent of labels extracted from
press releases, showing that press releases capture a common language for classification among
audiences in this domain. Of the labels used in
both press releases and in Gartner reports, over
75 percent appeared first in press releases.
We also investigated whether affiliations in
press releases target a specific audience or whether
they reflect the general identity of the organization. This question is especially important because different audiences can have different preferences regarding classification (Pontikes 2012).
We compared claims claimed in press releases
and company websites from the same time period
(recorded in the archived web) and those claimed
in annual reports (10-K forms) for public companies. Organizations identified with the same
labels in press releases and on websites 71 percent
of the time and in 10-K forms 81 percent of the
time. This indicates that label assignments from
press releases reflect an organization’s general
market identity.
This classification contains both lenient and
constraining categories. Constraining categories
evoke specific expectations. For example, <entertainment software> refers to producers of video
game software. This category has a trade association, the Entertainment Software Association (or
ESA), that tracks membership and promotes the
interests of members, such as lobbying the government on issues like video game ratings. This
324
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
Figure 4: Sample press release identity statements with category affiliations.
category has very low overlap with others. Companies that produce other types of software for
entertainment—such as digital video or audio—
do not affiliate with this label. Lenient categories
broadly overlap with many other categories, and
their meaning is a frequent source of discussion
among industry insiders. For example, industry
analyst James Kobielus of Forrester Research describes in a blog post titled “What’s Not BI?” how
audience members struggle to struggle to derive
meaning from the lenient <business intelligence
(BI)> category:
One of the favorite pastimes of BI analysts everywhere . . . is defining and
redefining this uber-category known
as BI. What exactly is it? Or rather—
considering that almost every data
sociological science | www.sociologicalscience.com
325
management technology has been swept
into BI’s gravitational orbit at one
time or another by somebody somewhere—
what is BI not? What’s analytics?
What are decision support systems?
(Kobielus 2010)
Lenient labels are vague and have porous boundaries, but they are not necessarily marginal or declining in importance. In many cases, they grow
large and become prominent. For example, the lenient <customer relationship management> label
received 149 mentions in the Wall Street Journal
from its first appearance in 1998 though 2008.
But <electronic design automation>, a prominent constraining label, was mentioned only 33
times from its first appearance in 1988 through
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
2002 (its last mention). Some of the most important categories in this classification are lenient.
Our model requires that label space only include labels at the same hierarchical level. Labels
analyzed should not be supercategories or subcategories of one another. This condition holds
for the labels included in this analysis. Lenient
categories included in this analysis are not superordinate categories. In a hierarchy, a superordinate category sits above all subcategories in
a tree, such that if an organization belongs to a
subcategory, it also belongs to the supercategory.
This type of hierarchical relationship is uncommon in our empirical context, as is often the case
for informal classification. For example, the extension of <business intelligence> overlaps with
that of <sales-force automation>, but not all
<sales force automation> members are labeled as
<business intelligence>. Furthermore, some organizations only affiliate with lenient categories.
Lenient categories overlap broadly, sometimes
with more than 100 other categories; but the
categories they overlap with are not nested.
labels. This created a file of organizations’ labels for each year during 1990–2002. The final
data contain information on 456 labels and 4,566
organizations over 18,192 organization-years.
Feature Space
Our empirical analysis positions organizations in
a feature space based on technical capabilities,
which we call knowledge space. This space is a
subset of all feature values that characterize organizations. We use patents to represent knowledge
space. Patents do not capture the complete set
of relevant features in this domain, but patented
technology reflects an important subset of innovative activity among software producers. Patents
have been widely used in previous research to
identify an organization’s technological position
and to measure relative similarities in a technical space (Jaffe 1986 1989; Podolny et al. 1996;
Stuart and Podolny 1996; Sørensen and Stuart
2000).
Technological innovation has been at the heart
of the software industry’s growth, and a fair
amount of inventive activity can be traced through
patents. Patents influence venture-capital financing, IPOs, and successful acquisition (Mann and
Sager 2007; Cockburn and MacGarvie 2009). Even
small organizations patent, hoping to exclude
competitors (Mann 2005). Despite controversy
about the relevance of patents in the software
industry, a sizable number of software organizations received patents (20 percent in the press
release data).17
Patents provide a good basis to measure positions in a technological feature space, for several
reasons. First, they reflect an organization’s technical capabilities. Second, a independent party
verifies them. Third, the patenting process is
very weakly influenced (if at all) by an organi-
Data and Methods
Label Space
Testing these propositions requires data on label
assignments. We use data complied by Pontikes
(2008) based on identity statements in press releases issued by software organizations (for examples, see Figure 4). This identifies times at which
organizations adopt new labels and drop existing
labels.
A compilation of press releases issued between
1990 and 2002 in Businesswire, PR Newswire, and
Computerwire with at least three mentions of the
word “software” served as the initial source of
data: 268,963 press releases. A combination of
custom-coded programs for text matching and
visual examination of the outputs of these programs yielded records for 4,566 software organizations that issued press releases during this period.
The identity statements made by these organizations indicate category membership. Pontikes
(2008) assembled an extensive list of software
labels from articles in Software Magazine and
Computerworld and from inspection of the firms’
identity statements, and used text-matching programs to search all identity statements for these
sociological science | www.sociologicalscience.com
17 Patents have been controversial in software.
Gottschalk v. Benson in 1972 ruled that software programs were algorithms that could not be patented. However, this decision was mostly overturned in Diamond
v. Diehr in 1981, which found that a software program
could be patented if it was embedded within an apparatus. After a series of cases that increasingly supported
the patentability of software, a 1995 ruling essentially
reversed Gottschalk, and the last barrier to patenting
pure software was overturned in 1998. Despite this, the
approval of software patents was a routine practice long
before the courts recognized it (Cohen and Lemley 2001;
Cockburn and MacGarvie 2009; Mann 2005).
326
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
zation’s product-market categorization. In this
context, market categories are only loosely based
on technologies. For example, <collaboration
software> combines technologies based on video,
GUI (graphical user interface), and mathematical
algorithms. Feature-space data based on product
evaluations by industry analysts would be much
more likely to be influenced by how the organization positioned itself using product-market
classification (not to mention that these data are
not systematically available). A knowledge space
based on patents captures a critical subset of
feature space for this domain that can be measured independently from label space. Through
citations, patents provide a historical record of
knowledge-space positions and proximities.
The downside to using patents is that not all
software organizations patent. We conduct our
main analyses on the subset of organizations that
are active in knowledge space (those that have
previously patented). The press release data contain 789 organizations with at least one patent
across 4,012 organization-years. We run additional analyses on all organizations in the press
release data, coding those that are not active in
knowledge space (those without patents) as having zero knowledge-space proximity to all labels.
The results are consistent across both analyses.
Patent citations come from the National Bureau of Economic Research patent-data project
(Hall et al. 2001). Data were updated to include
patents through 2002.18 The U.S. Patent and
Trademark Office issues patents for new, unique,
and nonobvious inventions. All patents must cite
the relevant “prior art” on which their invention
builds, and the prior-art citations indicate the
knowledge foundation for the patent at hand.
Two patents that cite the same patent as prior
art are more similar in knowledge space than
pairs that lack common citations.
The patent office requires that inventors’ claims
be focused and narrow. Inventors must cite any
relevant patents of which they are aware, and the
patent examiner can also add citations to ensure
comprehensive citation to prior art (Alcácer and
Gittleman 2006). For some studies, it is important that the citations to prior art accurately
18 The data updates can be found at http://elsa.
berkeley.edu/~bhhall/patents.html. Data were first
updated through 2002, then through 2006. This research
uses the 2002 update.
sociological science | www.sociologicalscience.com
327
reflect the inventor’s knowledge; and citations
added by the examiner are problematic. This
does not pose a problem for our study, because
we use patents to locate organizations in a knowledge space. Citations added by an examiner help
refine the patents’ positions.
Patent officers assign patents to a main class
and one or more subclasses. There are about
400 classes. The National Bureau of Economic
Research has created a higher-level classification
system of six classes: Chemical, Computers and
Communications, Drugs and Medical, Electrical
and Electronics, and Other (Hall et al. 2001).
This study uses all patents granted in the Computers and Communications class between the
years 1990 and 2002 to construct knowledge space
for the software industry. This broad class includes patents relevant to software.
Following Pontikes (2008), we construct knowledge space using a five-year window of all patents
in the Computers and Communications class. We
use all patents relevant to software, including
those issued to software organizations, individual
inventors, universities, and nonsoftware organizations over a moving five-year window, including
patents that were applied for (that were subsequently granted) in the current year and four
years prior.
Independent Variables
Here we provide the details about the measurement of the two key explanatory variables.
Leniency. To represent leniency, we need to measure the proximities among categories in the domain. We adopt Pontikes’s (2008) measure, which
uses category overlaps in label space to indicate
similarity between categories. A category is more
similar to another in label space if many of its
members also belong to the other category.
Leniency is the product of contrast and a positive function of the number of categories with
which the focal category overlaps. Contrast is
the average label-based typicality of its members.
It measures the extent to which a label comprises typical or atypical members (Hannan et al.
2007).19 To measure contrast, we first must compute label-based typicalities, ϑ(l, x, y) from label
19 It is important to incorporate contrast into the measure of leniency. Otherwise the membership of a few
members would have outsized influence. For example, a
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
assignments. We assume that an object that is assigned to multiple categories is less typical of each
and that this diminution is exacerbated when the
categories lie far apart in the label space. For
example, an establishment that is both a French
restaurant and a car wash would be very atypical
of each label.
In accordance with our theory, we take into
account producers’ partial affiliations with labels.
What we call label-profile typicality, denoted by
ϑ(l, x, y), is based on (1) the number of times a
producer affiliates with a focal category in a given
time period, compared to the number of times it
affiliates with other categories, and (2) whether
a producer is a member of multiple similar or
multiple distant categories. The more a producer
affiliates with a focal category, the higher its labelprofile typicality. The less it affiliates with other
categories—especially other dissimilar categories,
the higher is its typicality in the focal category.
Label-profile typicality ranges over [0, 1]. This
construction follows Kovács and Hannan (2011)
but uses a generalization that builds on Pontikes
(2008). The first step calculates the similarities of
all pairs of categories, in notation simL (l, l0 , u),
based on how many organizations are assigned
membership in both categories in the time interval u. The second step uses a Shepard (1987)
construction to calculate distance between a pair
of categories based on label profiles, in notation,
d˜L (l, l0 , u), as a Euclidean distance, as follows:
N y, u ∃ h [(h > 0) ∧ ∀ l, l0 ,
0
eL
t [(simL (l, l0 , u) = e−h d (l,l ,u) ]].
(1)
We use measured similarities (based on co-occurrence) and a setting for the parameter h to obtain
dL . After experimentation with values for h ranging over the integers from 1 to 5, we found that
similar patterns of effects of theoretically relevant
variables (significance levels do vary, however).
We set h = 3, which provides the best overall
model fit.
The next step calculates typicality, ϑ(l, x, u),
from label profiles using the idea that a producer’s
typicality in applied categories declines with the
application of each new label, and the decline is
greater when the newly added label lies far from
the already applied categories. We calculate label
label with a hundred typical members and one member
with broad overlap would appear as lenient.
sociological science | www.sociologicalscience.com
328
profiles by taking into account that firms often
release multiple press releases at multiple times
during a year, and each provides information on
labeling. Rather than only including whether a
firm claims a label in a given time period, we
weight the relative frequency of claims to each
label. For example, if an organization claims
label A in one press release and B in three press
releases in the same year, for a total number of
4 label mentions, then we calculate l(A, x)=1/4
and l(B, x)=3/4. We calculate ϑ as follows:20
ϑ(l, x, u) =
`(l,x,u)
P
.
`(l,x,u)+ l0 ∈L `(l0 ,x,u)·deL (l,l0 ,u)
(2)
A label’s contrast is the average label-based typicality of its members:21
P
x:ϑ(l,x,u)>0 ϑ(l, x, u)
C(l, u) =
.
(3)
|{x : ϑ(l, x, u) > 0}|
We calculate leniency as the product of inverse
contrast and a positive function of the number of
categories overlapped:
len(l, u) = (1 − C(l, u)) · ψ(nl,u )
P L
= (1 − C(l, u)) · ln( l0 df
(l, l0 , u)),
(4)
where nl,u denotes the number of other categories
whose memberships overlap with l. The function
ψ(nl,u ) is computed based on a summation of the
distance between l and each label l0 with which
it overlaps. We use a log transform because the
interpretability of a label ought to drop more
sharply when the number of overlaps rises in the
lower range than at higher levels. Our decision
to weight the number of categories overlapped by
their distances from the focal label follows the
spirit of the construction of ϑ, namely, that combining dissimilar categories causes a greater loss
in distinctiveness than combining similar ones.
We separate leniency into two pieces, instead
of using the continuous measure, because the
key propositions depend on one label being sufficiently more lenient than another. We break the
distribution of leniency at 2.7, the approximate
mid-point of the leniency of producers’ labels,
20 Kovács and Hannan (2011) treat `(l, x, u) as a
dummy variable; Pontikes (2008) implicitly assumes all
deL (l, l0 , u) = 1.
21 We use the standard notation | · | to denote the cardinality of a set, the number of distinct elements.
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
and treat high (low) leniency as values above
(below) the breakpoint.
Producer’s proximity to labels in knowledge space.
The other main variable to be defined is the proximity (inverse distance) of a producer’s position
in knowledge space from a label. Recall that
we assume that the hazards of adding and dropping labels are negative exponential functions of
the distance of an object’s position in feature
space from the schema for the label (Postulate 4).
We lack knowledge of schemas. So we proceed
indirectly by measuring a label’s position in feature space in terms of the set of positions of the
objects that lie in the label’s extension. Then
we measure an object’s position in feature space
relative to the label’s extension.
We measure positions in knowledge space using their patents’ prior art citations. Patents
that share citations build on similar knowledge.
Therefore we use citation overlaps to position
patents relative to one another in knowledge space
(Podolny et al. 1996). We use the patent citation
network of all patents in the Computers and Communications class (Hall et al. 2001). We measure
similarity from prior-art citations (Podolny et al.
1996; Podolny and Stuart 1995). The similarity
of patent i to patent j, αij , is defined as sij /si ,
where sij denotes the number of shared citations
between patents i and j, and si denotes the total
number of citations by patent i.22 We can capture more fine-grained detail by also considering
22 We
have the choice of whether to use asymmetric
or symmetric measures of similarity. With asymmetric
similarities, patent i can be more similar to patent j than j
is to i. Some research in cognitive psychology supports the
use of asymmetric similarities. Studies show that people’s
assessments of similarity is asymmetric across a number
of domains (countries, figures, letters, signals) (Tversky
1977). Competition can also be asymmetric, for example,
a specialist competes more strongly with a generalist than
vice versa; because of this asymmetric measures have
previously been used to define technological niches among
firms using patent citation data (Stuart and Podolny
1996). There are also reasons to use symmetric similarities
(defined as the number of overlapping citations divided
by the number of citations made by both i and j: αij =
sij /si sj ). Symmetric similarities translate into symmetric
distances in feature space, which means that distances
between two points in feature space will not depend on
directionality. This is an important property for our
formal model of feature space and label space. The choice
does not materially affect our results. Weighing these
options, we chose to use asymmetric patent similarities
in our primary analysis to comport with standards of
existing empirical research.
sociological science | www.sociologicalscience.com
329
second-degree similarity, which takes into account
whether a patent stands within two degrees of separation from another patent. This brings into the
picture common dependence on relevant patents
that were issued to nonsoftware organizations
such as universities. We compute second-degree
similarity between of patent i to k by multiplying their similarities to all third patents, j, and
choosing the j that maximizes the similarity of i
and k: ρik = maxj|j6=i,j6=k (αij · αjk ).
The degree to which interested agents regard
a patent as tied to a label presumably depends
on how strongly the organization that issued the
patent is affiliated with the label. If the inventor
firm has a low level of typicality in a label, its
patents should similarly be considered only partially representative of the label. To reflect this
view, we define a patent’s typicality in a label
as equal to the label-based typicality in l of the
firm to which the patent was issued, in notation
ψ(l, i, u) = ϑ(l, x, u) I(i, x, u), where I(i, x, u) is
an indicator variable equal to one if patent i is a
member of the set of organization x’s patents (in
notation pxu ) and equal to zero otherwise. The
proximity (inverse distance) of an organization’s
position in feature space to a label, p(l, x, u), is
P
i∈pxu
P p(l, x, u) =
j|(j ∈p
/ xu ) ρij · ψ(l, j, u)
|pxu |
,
(5)
where the second summation is restricted to the
patents affiliated with the label l. We also use
Equation 5 to calculate the proximity (inverse
distance) of a producer’s position to all labels that
it currently claims, pC (x, u), and to all labels it
currently does not claim, pN C (x, u) (to be used
as control variables). Because of skew in the
distribution of the distances, we use (natural)
logarithmic transformations of these variables in
our empirical analysis.23
23 The distance between an organization’s patents and
the patents associated with a category is a distance between two sets. As such, it might seem that we should use
the Hausdorff distance, which we employ in our formal
theory for distances between schemas. However, in this
case, an organization can belong to multiple categories, so
its set of patents can include those that are not relevant to
category i, but are relevant to a category j. In this case,
the Hausdorff distance would only consider the patent
that was farthest from the set of patents associated with a
category, which often will refer to a patent that is relevant
to a different category.
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
Table 1: Descriptive Statistics for Tests Involving the Hazard of Adding a Label. (N = 1, 434, 096)
Variable
Mean
0.004
0.006
0.004
0.002
0.745
0.105
Org. adds a label
K-space prox. to focal label
K-space prox. to const. focal label
K-space prox. to len. focal label
K-space prox. to other labels org. not claimed
K-space prox. to labels org. claims
A producer’s distance from a label’s feature
profile can change in three ways: (1) it can move
away from a stationary profile, (2) other producers can move while the focal producer remains
in place, or (3) both the focal producer and the
other profile members move but in different directions. To explore this in supplementary analyses,
we also create a measure of an organization’s stationarity in knowledge space based on citation
overlap between the organization’s patents in the
current year and its patents in the previous four
years.
Controls
Social factors, such as whether a category is
resource-rich or otherwise popular, can also affect
label adoption. People and organizations sometimes engage in herding behavior, where high
levels of adoption in one period lead to increased
rates of adoption in the next period (Strang and
Soule 1998). This effect can result either because
agents follow each other’s actions, as in the case of
fads (Lieberson 2000; Meyersohn and Katz 1957),
or because the number of organizations in a category signals an unmeasured factor, for example
demand for a specific product. To account for
these effects, we include the label’s fuzzy density
(the number of organizations that are members
of the label weighted by typicality) to control for
the label’s size in the previous period. The rate
at which a label is catching on can also affect
adoption patterns (Berger and Le Mens 2009).
Therefore we also control for the number of new
affiliations with the label and number of drops in
the previous period, both weighted by typicality.
We also include controls for the leniency of the
label and tenure of the label measured since the
beginning of our records, 1990.
sociological science | www.sociologicalscience.com
330
Std. Dev.
0.060
0.045
0.038
0.024
0.934
0.251
Min.
Max.
0
0
0
0
0
0
1
2.543
2.543
2.073
4.003
2.462
Organization-level controls include the number of labels in which it has nonzero membership,
the number of other labels in the industry, the
count of its patents (to account for general inventive activity), and the time since the organization
has last added or dropped any label. Venture
capitalist investors may influence an organization to add or drop market categories (Pontikes
2012). Therefore we also control for whether the
organization has recently received venture capital
financing. Because old and large organizations
might not pay as much attention to feature-based
fit, perhaps because they receive less scrutiny,
we control for the organization’s tenure in our
data, to account for age, and for whether it was
ranked in Software Magazine’s top 500 software
companies (based on revenue) to account for size.
To test whether there is a systematic relationship
with knowledge-space proximity to a label, we
also estimated specifications that include interactions between these variables and proximity.24
Tables 1 and 2 provide descriptive statistics
for the independent variables in our analysis.
Dependent Variables
We analyze events of adding and dropping labels
in continuous time. We code adding a label as
occurring in the first year in which an organization lists that label in press releases, and the
dropping of a label as occurring in the last year
an organization lists it (if the observation is not
right-censored at that time).25 Seventy percent
24 These
interaction effects are not significant, indicating
that there is not a detectable difference in the influence
of feature similarities on label assignments based on organization age or size. Importantly, the key effects persist.
25 Label claims from the initial year an organization
appears in press releases are not counted as an add, and
claims from the final year are not counted as a drop.
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
Table 2: Descriptive Statistics for Tests Involving the Hazard of Dropping a Label. (N = 10, 908)
Variable
Mean
0.403
0.047
0.024
0.022
0.915
0.731
Org. drops a label
K-space prox. to focal label
K-space prox. to const. focal label
K-space prox. to len. focal label
K-space prox. to labels org. is not claimed
K-space prox. to other labels org. claims
of organizations add and drop at least one label
during the time period studied. Ninety-five percent of patenting organizations add and drop at
least one label. Twenty percent of all organizations and 45 percent of patenting organizations
add more than five labels. Twenty percent of all
organizations and 40 percent of patenting organizations drop more than five labels.
Stochastic Specifications
and Estimation
We test these hypotheses using event-history analysis. We estimate the effects of proximity and
leniency on the hazards with standard piecewiseconstant specifications. We updated covariates
for each time piece. In both forms of analysis,
pieces are defined for less than one year, [1–2)
years, [2–4) years, and 4 years or greater. Standard errors are clustered by label.
Adding labels. In analyses of the hazard of adding
a label, the risk set consists of all dyads of an
organization that has previously patented and
one of the labels with which it is does not affiliate. There are 337,800 organization-label dyads
over 1,434,096 organization-label-years, and 5,266
events of adding a label. We also conduct additional analyses using data including all organizations in the press release data, whether they
patent or not. In this case, we assign nonpatenters knowledge-proximity scores of zero for all
labels. This larger data set contains 1,893,569
organization-label dyads over 6,485,521 organization-label-years and 15,103 events of adding
a label. Dyads enter the model in the first year
the organization or label enters the data (whichever comes later). Duration is the time since the
organization-label dyad exists in these data.
sociological science | www.sociologicalscience.com
331
Std. Dev.
0.491
0.144
0.106
0.104
0.965
0.928
Min.
Max.
0
0
0
0
0
0
1
2.499
2.499
1.818
4.003
4.014
Dropping labels. In analyses of the hazard of dropping a label, the risk set contains all dyads of an
organization that has previously patented and
their labels: 5,967 dyads over 10,908 years, with
4,397 events of dropping a label. Again we also
included the nonpatenters in additional analyses;
the entire data set contains 21,589 organizationlabel dyads over 39,359 organization-label-years,
with 13,880 events of dropping a label. We analyze spells during the years 1990–2001. Dyads
enter the risk set in the first year the organization affiliates with the label. Duration is the time
elapsed since the organization first affiliates with
the label.
Hypotheses
We test four hypotheses in this empirical context based on our theoretical propositions. The
first and second hypotheses concern the relationship between feature-space positions and label
affiliations (prop. 1).
Hypothesis 1. A producer’s hazard of adding
a label increases with its proximity in knowledge
space to the cluster of producers associated with
the label.
Hypothesis 2. A producer’s hazard of dropping
a label decreases with its proximity in knowledge
space to the cluster of producers associated with
the label.
The third and fourth hypotheses concern the
effects of leniency (prop. 2).
Hypothesis 3. Proximity in knowledge space
to the producers associated with a constraining
label has a stronger positive effect on the hazard
of adding that label than does proximity to a
more lenient label.
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
Hypothesis 4. Proximity in knowledge space
to the producers associated with a constraining
label has a stronger negative effect on the hazard
of dropping that label than does proximity to a
more lenient label.
Results
Adding Labels
We analyze the effects of knowledge space proximity on the hazards of adding a label. Table
3 reports estimations on the hazard of adding a
label for the independent variables in the analysis (all estimations include controls; full models
available upon request). The first model in Table 3 reports tests of H1. This analysis considers
dyads of organizations that have patented and
the labels that they do not already claim. The
results support H1: an organization’s knowledgespace proximity to a label has a positive effect
on the hazard of it adopting this label (p < 0.01
in a z-score test). An organization that is two
standard deviations above the mean on proximity to a label is 10 percent more likely to adopt
the label, as compared to one at mean proximity,
according to this estimate.
Next we analyze whether the effects of proximity depend on leniency. The second model of
Table 3 provides tests of H3. The results support
this hypothesis: proximity to a constraining label has a strong positive effect on the hazard of
adding it (z-score test significant at p < 0.01).
However, the effect of proximity is smaller and
insignificant when the proximate label is lenient.
This specification fits better than the one in
model 1 (the likelihood-ratio test is significant
at p < 0.01). This indicates that feature values
matter less for affiliation with lenient labels.
We also explored some possible confounding
effects. One question might be whether proximity
drives the effect, or whether it just reflects the
consequences of general exploration in the space.
To test against this alternative, we include an effect of the organization’s proximities to all other
labels it does not affiliate with, and to all other
labels it does affiliate with (model 3). The results show that general exploration in knowledge
space has a positive effect on an organization’s
propensity to add any label. But the effect of
exploration is smaller by an order of magnitude
sociological science | www.sociologicalscience.com
332
as compared to the effect of proximity. An organization’s proximity to labels it already claims
does not have a statistically significant effect on
adding a new label. Importantly, our main results
persist. Estimations run on all organizations in
the press release data, where nonpatenters are
assigned knowledge proximity zero, gives similar
results (model 4). Results are also similar when
we control for leniency using a binary variable
that indicates whether the organization affiliates
with a high-leniency label, defined using the same
cutoff.
Dropping Labels
The next set of analyses explores the effects of
knowledge-space proximity on the hazard of dropping a label. Table 4 reports estimations on the
hazard of dropping a label for the independent
variables in the analysis (all estimations include
controls; full models available upon request). The
first model in Table 4 provides a test of H2. This
analysis considers dyads of organizations and labels that they affiliate with. The results support
H2: the higher an organization’s proximity to a
label in knowledge space, the less likely it will
drop its affiliation with that label (p < 0.05 in
a z-score test). This specification also includes
an effect of the organization’s knowledge space
proximity to all other unclaimed labels. This
effect is positive. In some estimations (Table 4,
models 4 and 5), the effect is positive and significant. Together, these results illustrate the
dynamic link between feature space location and
label assignments: the closer is an organization
is to a label it already claims, the less likely it is
to drop this label. The closer it is to other labels
it does not claim, the more likely it is to drop a
claimed label (and adopt the new label to which
it has become proximate).
The second model in Table 4 provides tests of
H4, concerning the effects of leniency on hazards
of dropping a label. The coefficients have the predicted signs but they do not differ significantly
according to a chi-square test. Consistent with
the hypothesis, proximity to a constraining label
has a negative and significant effect on the hazard
of dropping (z-score test significant at p < 0.01),
whereas the effect for lenient labels is weaker and
not significant. To explore this effect further, we
increased the leniency threshold from 2.7 to 3.0,
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
Table 3: Effects of Proximity and Leniency on the Hazards of Adding a Label (ML Estimates of
Piecewise-Continuous Hazard Models)
Hyp. 1
(Patenters)
(1)
K-space proximity
Focal label
Hyp. 3
(Patenters)
(2)
Hyp. 3
(Patenters)
(3)
Hyp. 3
(All Orgs)
(4)
1.548†
(0.208)
0.442
(0.368)
1.470†
(0.195)
0.205
(0.370)
0.153†
(0.025)
0.063
(0.057)
−23,717.94
31
1.464†
(0.199)
−0.069
(0.324)
0.135†
(0.023)
0.051
(0.053)
−75,284.90
31
1.049†
(0.243)
Constraining focal label
Lenient focal label
Other not claimed label(s)
Label(s) org claims
Log pseudo-likelihood
Degrees of freedom
−23,769.69
28
−23,754.75
29
Note: All specifications include: label controls for leniency, fuzzy density, recent adds, recent drops,
and label tenure (since 1990); organizational controls for the number of labels the organization claims,
the number of other labels in the industry, a count of the organization’s patents, tenure (since 1990),
whether it ranked in the Software 500, recently received venture capital financing, and the time since
adding any label; year fixed effects and duration pieces.
Patenters: 337,800 org-label dyads over 1,434,096 org-label-years; 5,266 events of adding a label.
All orgs: 1,893,569 org-label dyads over 6,485,521 org-label-years; 15,103 events of adding a label.
∗
p < 0.05; † p < 0.01
and the results strongly support the hypothesis
(model 3). This effect persists when we include
a control for an organization’s proximity to the
other labels it already claims (model 4). An estimation on all organizations in the press-release
data (model 5) also shows similar effects. In addition, consistent results hold when we control
for leniency using a binary variable.
This analysis highlights the importance of the
existential quantification in the definition of leniency: the predictions about leniency hold only
provided that one category is sufficiently more
lenient than another. In this analysis, these effects are evident when the leniency threshold
is increased, suggesting that there exists a leniency threshold above which the link between
the spaces breaks down. Overall, this analysis
provides some support for H4. Together with
the results reported here, it provides additional
evidence that leniency weakens the relationship
sociological science | www.sociologicalscience.com
333
between feature values and label affiliations. It
also suggests that feature-space proximity has a
stronger influence on the propensity to drop a
lenient label as compared to the propensity to
adopt a lenient label.
Proximity to other labels the organization affiliates with has a negative effect on the hazard
of dropping the focal label, and it is significant in
analyses of all organizations (z-score test significant at p < 0.01). Again, the size of the effect is
substantially smaller than the effect of knowledgespace proximity to the focal label (from the dyad).
This indicates that there might be some symbiotic
effects in terms of the feature profiles of labels a
producer simultaneously claims.
One potential concern is that organizations
might generally be more proximate to lenient
labels and that this drives the results in both
the estimates for both adding and dropping labels. Our data show that the distributions of
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
Table 4: Effects of Proximity and Leniency on the Hazards of Dropping a Label (ML Estimates of
Piecewise-Continuous Hazard Models)
Hyp. 2
(Patenters)
(1)
K-space proximity to:
Focal label
Hyp. 4
(Patenters)
(2)
Hyp. 4
(Patenters)
(3)
Hyp. 4
(Patenters)
(4)
Hyp. 4
(All Orgs)
(5)
0.036
(0.020)
−0.607†
(0.212)
−0.251
(0.260)
0.038
(0.020)
−0.716†
(0.223)
−0.035
(0.180)
0.038
(0.020)
−6,705.53
29
−6,705.05
30
−6,702.67
30
−0.698†
(0.218)
−0.045
(0.177)
0.062∗
(0.031)
−0.035
(0.031)
−6,701.61
31
−0.680†
(0.220)
−0.215
(0.205)
0.108†
(0.027)
−0.079∗
(0.031)
−24,678.40
31
−0.422∗
(0.186)
Constraining focal label
Lenient focal label
Not claimed label(s)
Other label(s) org. claims
Log pseudo-likelihood
Degrees of freedom
Note: All specifications include: label controls for leniency, fuzzy density, recent adds, recent drops,
and label tenure (since 1990); organizational controls for the number of labels the organization claims,
the number of other labels in the industry, a count of the organization’s patents, tenure (since 1990),
whether it ranked in the Software 500, recently received venture capital financing, and the time
since dropping any label; year fixed effects and duration pieces. Model 3 imposes a higher leniency
threshold (see text).
Patenters: 5,967 org-label dyads over 10,908 org-label-years; 4,397 events of dropping a label.
All orgs: 21,589 org-label dyads over 39,359 org-label-years; 13,880 events of dropping a label.
∗
p < 0.05; †; p < 0.01
knowledge-space proximities to constraining and
lenient labels are similar.26
Together these results for dropping labels and
those reported for adding labels show how featurebased proximities shape categorical affiliations in
a dynamic context. These effects are illustrated
in Figures 5 and 6. For constraining labels, there
is a strong relationship between an organization’s
proximity to the label and an increased hazard of
adding the label, and decreased hazard of dropping the label. For lenient labels, the link is
weaker.
Additional Tests
We conduct additional tests of these results. Our
model proposes a coupled ecology between feature space and label space, where both positions
of organizations and meanings of categories shift
26 See
Tables 1 and 2.
sociological science | www.sociologicalscience.com
334
over time. Results show that the relative distance in feature space between an organization
and category representation affects whether an
organization adds or drops the category label.
We further explore this by including in the estimations a measure of organization stationarity,
which measures the percentage of the organization’s current year citations that the organization
has previously cited (in the past four years). Organizations that score low on this measure have
moved more in knowledge space, whereas those
that score high are more stationary. We include
stationarity and an interaction between stationarity and knowledge proximity to the focal label as
covariates. Results show that neither stationarity
nor the interaction have a significant effect on an
organization’s propensity to add or drop labels.
Effects reported here persist (results available
upon request). This suggests that the reported
effects do not simply reflect an organization’s
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
Figure 5: Effects of knowledge proximity on the hazard of adding a label, by leniency (from Table 3,
model 3).
movement in feature space toward a stationary
category.
We also conduct additional tests to further
control for social factors that might affect an
organization’s propensity to add or drop labels.
We control for the number of organizations in a
label that have recently received venture-capital
financing. Such investments signal that an area
is promising and might give managers hope that
additional funds will follow. The hypothesized
effects are robust to the inclusion of this control.
Discussion
Constraint or Accident?
We noted that an observed pattern of label overlap can result from accidental circumstances or
from constraint. We suggest that these patterns
are not accidental and that they reflect differences
in constraint among labels. But if constraint is
the source of variation in the strength of categorical boundaries, where does this constraint
come from? Here we sketch an extension of the
sociological science | www.sociologicalscience.com
theory that links category overlap to appeal for
producers that change positions in label space.
Suppose that an audience member lacks detailed information about the feature values of two
producers but does observe label assignments.27
Consider two producers that belong to different
categories, one constraining and one much more
lenient with equal (high) typicality in their assigned category. Suppose that each is assigned
to some third label. Recall that our argument
implies that constraining categories normally lie
further from other categories then do lenient ones
(if the difference in leniency exceeds a critical
value). Then the definition of the typicality in
a label (ϑ) implies that the member of a constraining category generally experiences a greater
decline in its typicality its initial category than
is the case for the member of a lenient one. In
other words, taking on new label assignments
generally reduces typicality more for members of
constraining categories.
335
27 This example, where label assignments are known but
feature values are not directly observed, is common in
sociological research on classification.
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
Figure 6: Effects of knowledge proximity on the hazard of dropping a label, by leniency (from Table 4,
model 4).
This difference matters when the categories
involved have positive valuation. This means the
intrinsic28 appeal of a producer to a typical audience member is a positive increasing function
of its typicality in the meaning of the relevant
label (Hannan et al. 2007).29 So long as the concepts are positively valued, producers affiliated
with constraining labels lose more intrinsic appeal when they span concepts than is the case
for those affiliated with more lenient ones.
each producer adds the same third label, then the
member of the constraining category ends up with
lower intrinsic appeal in that label as compared
to the member of the more lenient category.
Proposition 3. Suppose that one producer belongs to one but not the other category and a
second producer has the mirror image pattern of
memberships and the typicalities are equal. If
Dynamics of Leniency
28 “Intrinsic” in this context refers to degree of fit to the
agent’s aesthetics. The theory on which we build holds
that it takes engagement of the audience by the producer
to convert intrinsic appeal to actual appeal.
29 Producers can have high (or low) typicality in constraining or lenient categories. The fact that appeal increases with a producer’s typicality in a category need
not imply that audiences prefer constraining categories.
Previous research shows that venture capitalists, an important audience in the domain of our empirical study,
prefer producers in vaguely bounded categories (Pontikes
2012).
sociological science | www.sociologicalscience.com
In this sense, members of constraining categories
have more to lose when they adopt feature values
that cause them to get assigned some additional
category membership(s). This cost is the source
of the constraint.
We proposed that proposition 2 implies that lenient labels tend to become more lenient and
constraining labels get more constraining. Here
we describe how to derive these implications. We
use an illustration of a domain with one constraining label and three lenient ones in Figure 7.
(For simplicity of representation we use a Euclidean space in this illustration.) In each case,
the shaded circle represents a schema (the set of
feature-value profiles that lie in these circles are
the schemas). The outer circles indicate the locations of the producers assigned the category label.
336
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
Category A is constraining because its members
more closely fit the schema (the outer circle is
closer to the shaded one than is the case for the
other categories) and because its membership
does not overlap the memberships of the others.
Categories B, C, and D are fuzzier (many members far from the schemas) and all three pairs
overlap.
Now consider three producers whose feature
values place them close to the producers assigned
one of the labels. As drawn, producers x1 and x2
stand at the same distance from the schema for A.
But x1 lies further from the memberships of the
other categories. Thus, according to the theory,
x1 has higher typicality in A than does x2 and
therefore has a higher hazard of being labeled
as an A. This means that the expected waiting
time to labeled membership is lower for x1 . In
typical cases, x1 will be labeled an A before x2 .
If this occurs, then the membership of A drifts to
the left, away from the memberships of the other
categories.
Next compare the situations for x3 and x4 .
As the illustrative figure is drawn, x3 lies outside
the membership of each category. In this respect
it is similar to x1 and x2 , and nearly the same
reasoning applies. This producer (x3 ) is slightly
further away from the schema for D than is x4 ;
and x3 is less proximate to the memberships of
all categories than is x4 . So, there is a high
likelihood that x3 will gain the label D. If this
happens, it will cause the membership of D to
move to the right, away from the other categories,
similar to the situation with category A. But x4
is much closer to the schemas for B and C (and A
for that matter) than is x3 . It has a high hazard
of becoming labeled as a member of B. If such
an event does occur, this creates a membership
overlap where one did not exist (as the figure
is drawn). Obviously this new overlap increases
the leniency of B and D. The situation is more
complicated than in the analysis of A, and the
net result depends on the flow of the stochastic
events. But there is no clear pattern as was the
case for the constraining category.
We estimated growth models (at the category
level) for leniency, specifications in which leniency
in a year depends linearly on leniency in the
previous year, the fuzzy density of the category,
its tenure, and yearly fixed effects. With either
category fixed effects or category random effects,
sociological science | www.sociologicalscience.com
we find a positive and significant effect of leniency
on the growth in leniency. In other words, the
higher is a category’s leniency, the higher is its
growth rate in leniency.
These dynamics likely feed back to the schemas.
For instance, as a constraining category drifts
away from the lenient ones, what is typical of this
category is also shifting away. If, as we expect,
audience members try to capture typicality in
forming schemas, then the schema will also drift
away from the others. This makes the initially
constraining category even more distinctive.
If a lenient label remains lenient or becomes
more so, then the broad (or broadening) overlap
lowers the chance that the audience reaches a
consensus about the association of a schema with
the label. Put differently, increased leniency diminishes agreement about the meanings. Pockets
might develop where audiences have experience
with organizations that have one type of overlap
with a lenient label, and they might develop a
schema with respect to that subset of activities.
This likely conflicts with the schema developed by
another audience exposed to other organizations
that are members of the lenient label but have a
different set of overlaps. This might lead to a lack
of consensus about the label’s schema for relevant
audiences. Hence lenient labels might evolve to
have ambiguous and uncertain meanings. At best
their schemas are defined at a very general level.
This dynamic is reflected in the trajectory of
a number of prominent lenient categories in our
analysis. For instance, this is the process James
Kobielus refers to in his “What’s not BI?” blog,
when he states that “almost every data management technology has been swept into BI’s gravitational orbit at one time or another by somebody
somewhere” (Kobielus 2010). Gartner comes to a
similar conclusion in their 2006 magic quadrant
report on <business intelligence>, noting that
<BI> is increasing in scope to encompass more
types of users, accessing more information sources
and a wider array of applications. <BI> becomes
even more lenient over time. In Gartner’s (2006)
report, they define <business intelligence> as
“the mission of BI . . . is the access to and analysis of quantitative information sources to deliver
insight that empowers decision makers”(Schlegel
et al. 2006). Their 2008 report provides an even
less constraining definition that does not specify
that the platform should connect the user with
337
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
B
A
x1
x4
x2
x3
C
D
Figure 7: Illustration of the dynamic implications of the theory.
disparate information sources: “BI platforms enable users to build applications that help organizations learn and understand their business”
(Richardson et al. 2008).
A similar trend is apparent in the evolution
of <enterprise resource planning (ERP)> and
<customer relationship management (CRM)>.
Initially lenient categories, both have increased in
leniency over time to the point that they no longer
even pose the constraint that category members
produce software. Gartner’s (2010) report on
<ERP> describes the market category as having
evolved from a technology product to a strategy:
These examples reflect the dynamic we described:
atypical members are more likely to be assigned
membership in a lenient label, leading to increased leniency and a broadening of the label’s
social meaning.
Conclusion
In the original definition, ERP systems’ functionality normally covers
the following areas: finance and accounting . . . purchasing, human resource management, sales or customer
order management, and operations
management. However, Gartner now
defines ERP in a broader sense as “a
technology strategy in which operational business transactions are linked
to financial transactions, specifically
general ledger transaction.” (Hestermann et al. 2010)
The editors at CRM magazine similarly describe
<CRM> as having evolved from a type of software to a business philosophy:
CRM, or Customer Relationship Management, is a company-wide business
strategy designed to reduce costs and
sociological science | www.sociologicalscience.com
increase profitability by solidifying
customer loyalty. . . Once thought of
as a type of software, CRM has evolved
into a customer-centric philosophy that
must permeate an entire organization.
(CRM Magazine 21 February 2010).
338
In this article we argue that social categorization is governed by an ecological dynamic in two
planes: feature space and label space. When an
actor changes its feature values or drops or adds
a label, these changes not only affect the focal
actor but also contribute to changing the meanings of social categories. This, in turn, can lead
to cascades of changes in classification, with entities shifting positions in both spaces in response
to movements of others. Such uncoordinated
movements might seem to lead to chaos. On the
contrary, we suggest that this dynamic can perpetuate a link between labels and sets of feature
values, if actors pay attention to the positions of
others and use labels accordingly. At the same
time, it is possible for categories to become lenient in label space, with high degrees of overlap
and vague boundaries. This creates an ecology
within label space, with proximities to other laAugust 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
bels disrupting a clear relationship between sets
of feature values and perceived typicality in the
category. The result is that leniency weakens the
relationship between feature-based similarity and
categorical assignments.
To investigate these ideas, we built a formal model relating positions in the two spaces.
Our theory implies that an object’s proximity to
labeled clusters in feature space influences the
adding or dropping of label affiliations in label
space. Furthermore, it predicts that for lenient
labels, the link between feature values and labels
weakens. The importance of expressing these
ideas in formal language comes through in our
analysis. It turns out to be tricky to formally
derive these conclusions, which suggests that they
might not be as straightforward as would appear
if expressed in natural language. Furthermore,
our analysis highlights the assumptions necessary
to arrive at our conclusions.
Our empirical analysis tests the theory’s implications in the software industry. We use data
on organizations’ patents to represent positions in
feature space and affiliations with market labels
from press releases to represent positions in label
space. The results support the new theoretical
propositions. We find evidence that when producers are proximate in knowledge space to a label
they affiliate with, they are less likely to drop it.
If they lie close in knowledge space to labels they
do not affiliate with, they are more likely to drop
any label and add the more proximate one. We
also find that feature-space proximity to lenient
labels has a weaker effect on adding a label, as
compared to proximity to constraining labels.
In deciding whether to affiliate with a label,
agents compare their own feature values with
those that already claim the label. This means
that the specific features associated with labels
can (and do) change over time. Even so, a link
between features and labels can persist. The relationship between features and labels is reinforced
simply when agents observe how others are classified and respond in kind. It is informative to note
what we do not assume: it is not necessary to
have strong mechanisms of boundary enforcement
or third parties such as critics and intermediaries
to retain a link between features and labels. It is
enough for agents to infer typicality from featurebased similarities to a labeled cluster and to label
based on typicality.
sociological science | www.sociologicalscience.com
339
At the same time, our study shows that increasing leniency weakens the relationship between features and label affiliations, resulting in
a partial decoupling between the two spaces for
lenient labels. With informal classification, both
constraint and leniency get reinforced as actors
independently navigate feature space and label
space. The result is that classification evolves to
contain a mix of categories: some constraining,
some lenient.
How labels become lenient is a different matter. Leniency likely emerges when atypical entities frequently are assigned membership in a
label and do not face censure. This suggests that
a lack of boundary enforcement can foster the
emergence of lenient labels in a classification. But
importantly, these elements are not necessary to
propagate lenient labels once they exist. This
means that once a label becomes lenient, it is
likely to remain that way, even with an audience that attends to the links between labels and
feature values.
This study also indicates that actors compare
positions in feature space and label space relative
to the positions of other actors in the domain.
It is proximity to objects that are affiliated with
a label—not necessarily fixed positions in feature space—that drives labeling. In this way, a
producer can become more or less proximate to
a labeled cluster if the cluster moves in feature
space, even if the producer does not move. We
believe this captures an interesting facet of social
classification: that feature values associated with
existing categories can (and do) change, and that
changes affect perceptions of the category.
In summary, this article explores dynamics
in market classification that arise when producers compare their feature positions to those of
competitors and use these comparisons to inform
market-label affiliation. Producers evaluate how
similar their organizations are to others when
deciding whether to adopt or drop a market label. This action does not go unnoticed by others
who are similarly comparing feature positions
and label affiliations. Thus emerges an ecology of
social categories, where positions in feature space
and label space change, but a link between them
persists, resulting in a dynamic and meaningful
system of classification.
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
References
Alcácer, Juan and Michelle Gittleman. 2006.
“Patent Citations as a Measure of Knowledge Flows: The Influence of Examiner Citations.”
The Review of Economics and
Statistics 88:774–779. http://dx.doi.org/10.
1162/rest.88.4.774.
Berger, Jonah and Gaël Le Mens. 2009. “How
Adoption Speed Affects the Abandonment of
Cultural Tastes.” Proceedings of the National
Academy of Sciences 106:8146–8150. http://
dx.doi.org/10.1073/pnas.0812647106.
Berger, Peter L. and Thomas Luckmann. 1967.
The Social Construction of Reality. New York:
Anchor Books.
Black, Max. 1937. “Vagueness: An Exercise in
Logical Analysis.” Philosophy of Science 4:427–
455. http://dx.doi.org/10.1086/286476.
Bowker, Geoffrey C. and Susan Leigh Star. 2000.
Sorting Things Out: Classification and its Consequences. Cambridge: MIT Press.
Clemens, Elisabeth and James Cook. 1999. “Politics and Institutionalism: Explaining Durability and Change.” Annual Review of Sociology 25:441–66. http://dx.doi.org/10.1146/
annurev.soc.25.1.441.
Cockburn, Iain M. and Megan J. MacGarvie.
2009. “Patents, Thickets and the Financing of
Early-Stage Firms: Evidence from the Software
Industry.” Journal of Economics and Management Strategy 18:729–773. http://dx.doi.
org/10.1111/j.1530--9134.2009.00228.x.
Cohen, Julie and Mark Lemley. 2001. “Patent
Scope and Innovation in the Software Industry.” California Law Review 89:1–57. http:
//dx.doi.org/10.2307/3481172.
DiMaggio, Paul J. 1997. “Culture and Cognition.” Annual Review of Sociology 23:263–
287. http://dx.doi.org/10.1146/annurev.
soc.23.1.263.
Durkheim, Émile. 1931. The Elementary Forms
of Religious Life. Oxford: Oxford University
Press.
sociological science | www.sociologicalscience.com
340
CRM Magazine. 21 February 2010. “What
is CRM?” http://www.destinationcrm.
com/Articles/CRM-News/Daily-News/
What-Is-CRM-46033.aspx.
Gärdenfors, Peter. 2004. Conceptual Spaces: The
Geometry of Thought. Cambridge: MIT Press.
Granqvist, Nina, Stine Grodal, and Jennifer
Wooley. 2013. “Hedging Your Bets: Explaining Executives’ Market Labeling Strategies in Nanotechnology.”
Organization
Science 24:395–413. http://dx.doi.org/10.
1287/orsc.1120.0748.
Grodal, Stine. 2011. “Boundary Labeling During Field Emergence: Inclusion and Exlusion
in Nanotechnology.” Working paper, Boston
University School of Management.
Hahn, Ulrike, Nick Chater, and Lucy B. Richardson. 2003. “Similarity as Transformation.”
Cognition 87:1–32. http://dx.doi.org/10.
1016/S0010--0277(02)00184--1.
Hall, Bronwyn, Adam Jaffe, and Manuel Trajtenberg. 2001. The NBER Patent Citations
Data File: Lessons, Insights, and Methodological Tools. Cambridge, MA: National Bureau of
Economic Research. http://dx.doi.org/10.
3386/w8498.
Hampton, James A. 2007. “Typicality, Graded
Membership, and Vagueness.”
Cognitive
Science 31:355–383. http://dx.doi.org/10.
1080/15326900701326402.
Hannan, Michael T., László Pólos, and Glenn R.
Carroll. 2003. “Cascading Organizational
Change.”
Organization Science 14:463–
482. http://dx.doi.org/10.1287/orsc.14.
5.463.16763.
Hannan, Michael T., László Pólos, and Glenn R.
Carroll. 2007. Logics of Organization Theory:
Audiences, Codes and Ecologies. Princeton:
Princeton University Press.
Hestermann, Christian, Chris Pang, and Nigel
Montgomery. 2010. “Magic Quadrant for ERP
for Product-Centric Midmarket Companies.”
Gartner.
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
Hsu, Greta. 2006. “Jacks of All Trades and Masters of None: Audiences’ Reactions to Spanning
Genres in Feature Film Production.” Administrative Science Quarterly 51:420–450. http:
//dx/doi.org/10.2189/asqu.51.3.420.
Leung, Ming and Amanda Sharkey. 2014. “Out
of Sight, Out of Mind: Evidence of Perceptual
Factors in the Multiple-Category Discount.”
Organization Science 25:171–184. http://dx.
doi.org/10.1287/orsc.2013.0828.
Hsu, Greta and Kimberly D. Elsbach. 2013.
“Explaining Variation in Organizational Identity Categorization.” Organization Science
24:996–1013. http://dx.doi.org/10.1287/
orsc.1120.0779.
Lieberson, Stanley. 2000. A Matter of Taste: How
Names, Fashions, and Culture Change. New
Haven CT: Yale University Press.
Hsu, Greta, Michael T. Hannan, and Özgecan
Koçak. 2009. “Multiple Category Memberships in Markets: An Integrative Theory and
Two Empirical Tests.” American Sociological Review 74:150–69. http://dx.doi.org/10.
1177/000312240907400108.
Jaffe, Adam B. 1986. “Technological Opportunity
and Spillovers of R&D: Evidence from Firms’
Patents, Profits, and Market Value.” American
Economic Review 76:984–1001.
Jaffe, Adam B. 1989. “Real Effects of Academic
Research.” American Economic Review 79:957–
970.
Keefe, Rosanna and Peter Smith (eds.). 1997.
Vagueness: A Reader . Cambridge Mass.: MIT
Press.
Kobielus, James. 2010.
“What’s Not BI?
Oh,
Don’t Get Me Started. . . Oops
Too Late. . . Here Goes. . . .”
Forrester
Blogs,http://blogs.forrester.com/james_
kobielus/10-04-30.
Koçak, Özgeçan, Michael T. Hannan, and Greta
Hsu. 2014. “Emergence of Market Orders: Audience Interaction and Vanguard Influence.”
Organization Studies 35:765–790. http://dx.
doi.org/10.1177/0170840613511751.
Kovács, Balázs and Michael T. Hannan. 2011.
“Category Spanning, Distance, and Appeal.”
Research Report 2081, Stanford Graduate
School of Business.
Kovács, Balázs and Rebeka Johnson. 2014. “Contrasting Alternative Explanations for the Negative Effect of Category Spanning: A Study of
Restaurant Reviews and Menus in San Francisco.” Strategic Organization 12:7–37. http:
//dx.doi.org/10.1177/1476127013502465.
sociological science | www.sociologicalscience.com
341
Mann, Ronald J. 2005. “Do Patents Facilitate
Financing in the Software Industry?” Texas
Law Review 83:1–71.
Mann, Ronald J. and Thomas W. Sager. 2007.
“Patents, Venture Capital, and Software Startups.” Research Policy 36:193–208. http://dx.
doi.org/10.1016/j.respol.2006.10.002.
McKendrick, David, Jonathan Jaffee, Glenn Carroll, and Olga Khessnia. 2003. “In the Bud?
Disk Array Producers as a (Possibly) Emergent
Organizational Form.” Administrative Science
Quarterly 48:60–93. http://dx.doi.org/10.
2307/3556619.
Meyer, John and Brian Rowan. 1977. “Institutionalized Organizations: Formal Structure as
Myth and Ceremony.” American Journal of
Sociology 83:340–63. http://dx.doi.org/10.
1086/226550.
Meyersohn, Rolf and Elihu Katz. 1957. “Notes on
a Natural History of Fads.” American Journal
of Sociology 62:594–601. http://dx.doi.org/
10.1086/222108.
Murphy, Gregory L. 2002. The Big Book of Concepts. Cambridge: MIT Press.
Negro, Giacomo, Michael T. Hannan, and Hayagreeva Rao. 2010. “Categorical Contrast and
Audience Appeal: Niche Width and Critical
Success in Winemaking.” Industrial and Corporate Change 19:1397–1425. http://dx.doi.
org/10.1093/icc/dtq003.
Negro, Giacomo and Ming D. Leung. 2013. “‘Actual’ and Perceptual Effects of Category Spanning.” Organization Science 24:684–696. http:
//dx.doi.org/10.1287/orsc.1120.0764.
Podolny, Joel M. and Toby E. Stuart. 1995. “A
Role-Based Ecology of Technological Change.”
American Journal of Sociology 100:1224–1260.
http://dx.doi.org/10.1086/230637.
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
Podolny, Joel M., Toby E. Stuart, and Michael T.
Hannan. 1996. “Networks, Knowledge, and
Niches: Competition in the Worldwide Semiconductor Industry, 1984–1991.” American
Journal of Sociology 102:659–689. http://dx.
doi.org/10.1086/230994.
Pólos, László and Michael T. Hannan. 2004. “A
Logic for Theories in Flux: A Model-Theoretic
Approach.” Logique et Analyse 47:85–121.
Pontikes, Elizabeth G. 2008. Fitting In or Starting New? An Analysis of Invention, Constraint,
Contrast, and the Emergence of New Categories
in the Software Industry. Ph.D. thesis, Stanford University, Stanford, CA.
Pontikes, Elizabeth G. 2012. “Two Sides of the
Same Coin: How Ambiguous Classification Affects Multiple Audience Evaluations.” Administrative Science Quarterly 57:81–118. http:
//dx.doi.org/10.1177/0001839212446689.
Porac, Joseph F., Howard Thomas, Fiona Wilson, Douglas Paton, and Alaina Kanfer. 1995.
“Rivalry and the Industry Model of Scottish
Knitwear Producers.” Administrative Science
Quarterly 20:203–227. http://dx.doi.org/
10.2307/2393636.
Rao, Hayagreeva, Phillipe Monin, and Rodolphe
Durand. 2003. “Institutional Change in Toque
Ville: Nouvelle Cuisine as an Identity Movement in French Gastronomy.” American Journal of Sociology 108:795–843. http://dx.doi.
org/10.1086/367917.
Richardson, James, Kurt Schlegel, Bill Hostmann,
and Neil McMurchy. 2008. “Magic Quadrant for
Business Intelligence Platforms, 2008.” Gartner.
Romanelli, Elaine. 1991. “The Evolution of New
Organizational Forms.” Annual Review of
Sociology 17:79–103. http://dx.doi.org/10.
1146/annurev.so.17.080191.000455.
Rosch, Eleanor H. and Barbara Lloyd (eds.). 1978.
Principles of Categorization. Hillsdale, New
Jersey: Lawrence Erlbaum Associates.
Rosch, Eleanor H. and Carolyn B. Mervis. 1975.
“Family Resemblances: Studies in the Internal
sociological science | www.sociologicalscience.com
342
Structure of Categories.” Cognitive Psychology 7:573–605. http://dx.doi.org/10.1016/
0010--0285(75)90024--9.
Ruef, Martin. 2000. “The Emergence of Organizational Forms: A Community Ecology Approach.” American Journal of Sociology 106:658–714. http://dx.doi.org/10.
1086/318963.
Ruef, Martin and Kelly Patterson. 2009. “Credit
and Classification: Defining Industry Boundaries in 19th Century America.” Administrative
Science Quarterly 54:486–520. http://dx.doi.
org/10.2189/asqu.2009.54.3.486.
Schlegel, Kurt, Bill Hostmann, Andreas Bitterer,
and Betsy Burton. 2006. “Magic Quadrant for
Business Intelligence Platforms, 1Q06.” Gartner.
Shepard, Roger N. 1987. “Toward a Universal Law
of Generalization for Psychological Science.”
Science 237:1317–1323. http://dx.doi.org/
10.1126/science.3629243.
Smith, Edward E. and Douglas L. Medin. 1981.
Categories and Concepts. Cambridge, MA: Harvard University Press. http://dx.doi.org/
10.4159/harvard.9780674866270.
Soltes, Eugene. 2009. “News Dissemination and
the Impact of the Business Press.” Dissertation,
The University of Chicago.
Sørensen, Jesper B. and Toby E. Stuart.
2000. “Aging, Obsolescence, and Organizational Innovation.” Administrative Science
Quarterly 45:81–112. http://dx.doi.org/10.
2307/2666980.
Strang, David and Sarah A. Soule. 1998. “Diffusion in Organizations and Social Movements:
From Hybrid Corn to Poison Pills.” Annual Review of Sociology 24:265–90. http://dx.doi.
org/10.1146/annurev.soc.24.1.265.
Stuart, Toby E. and Joel M. Podolny. 1996. “Local Search and the Evolution of Technological Capabilities.” Strategic Management Journal 17:21–38. http://dx.doi.org/10.1002/
smj.4250171004.
Tversky, Amos. 1977. “Features of Similarity.”
Psychological Review 84:327–351. http://dx.
doi.org/10.1037/0033--295X.84.4.327.
August 2014 | Volume 1
Pontikes and Hannan
An Ecology of Social Categories
Tversky, Amos and Itamar Gati. 1978. “Studies
of Similarity.” In Cognition and Categorization,
edited by Eleanor Rosch and Barbara Lloyd.
Hillsdale, New Jersey: Lawrence Erlbaum Associates.
van Demeter, Kees. 2010. Not Exactly: In Praise
of Vagueness. Oxford: Oxford University
Press.
Verheyen, Steven, James A. Hampton, and Gert
Storms. 2010. “A Probabilistic Threshold
Model: Analyzing Semantic Categorization
Data With the Rasch Model.” Acta Psychologica 135:216–225. http://dx.doi.org/
10.1016/j.actpsy.2010.07.002.
White, Harrison. 1981. “Where Do Markets Come
From?” American Journal of Sociology 87:517–
547. http://dx.doi.org/10.1086/227495.
Widdows, Dominic. 2004. Geometry and Meaning. Stanford CA: CSLI Publications.
Zuckerman, Ezra W. 1999. “The Categorical
Imperative: Securities Analysts and the Illegitimacy Discount.” American Journal of Sociology 104:1398–1438. http://dx.doi.org/10.
1086/210178.
Acknowledgements: We appreciate the extremely
helpful suggestions of Greta Hsu, Balázs Kovács,
Gaël Le Mens, László Pólos, Amanda Sharkey,
and Chris Yenkey. This study was supported
by the Chicago-Booth Graduate School of Business, the Stanford Graduate School of Business,
and the Charles E. Merrill Faculty Research
Fund at the University of Chicago Booth School
of Business, and by the John Osterweis and Barbara Ravizzi Faculty Fellowship at the Stanford
Graduate School of Business.
Elizabeth G. Pontikes: University of Chicago.
E-mail: elizabeth.pontikes@chicagobooth.edu.
Michael T. Hannan: Stanford University.
E-mail: hannan@stanford.edu.
sociological science | www.sociologicalscience.com
343
August 2014 | Volume 1